Simulation of log fitness change
Laure Olazcuaga, Nicolas Rode
21-January-2021
1 DATA
1.1 Load data
## Longitudinal data
data_sum <- loadlongitudinaldata(dataset = "DATA_Adults_G1G29.csv", rm_generation1 = 1,rm_generation2 = 7,rm_generation3 = 29)
## Phenotyping steps
data_G0 <- loadfitnessdata(dataset = "Selection_Phenotypage_G0_G7_G8.csv", generation = "G1")
data_G7 <- loadfitnessdata(dataset = "Selection_Phenotypage_G0_G7_G8.csv", generation = "G7")
data_G29 <- loadfitnessdata(dataset =
"PERFORMANCE_Comptage_adultes_G13G14G15G16G17G18G19G20G21G22G23G24G25G26G27G28G29.csv",
generation = "29")
head(data_sum)## Line Fruit_s Generation Phase N Nb_adults sd fitness
## 1 CE1 Cherry 2 first_prepool 20 9.15000 6.123939 -0.7819784
## 2 CE1 Cherry 3 first_prepool 10 15.30000 7.631077 -0.2678794
## 3 CE1 Cherry 4 first_prepool 8 15.00000 7.782765 -0.2876821
## 4 CE1 Cherry 5 first_prepool 6 14.50000 6.284903 -0.3215836
## 5 CE2 Cherry 2 first_prepool 20 7.75000 7.758696 -0.9480394
## 6 CE2 Cherry 3 first_prepool 7 14.42857 8.303757 -0.3265219
## se_fitness
## 1 0.1496562
## 2 0.1577228
## 3 0.1834415
## 4 0.1769518
## 5 0.2238577
## 6 0.2175215head(data_G0)## Treatment Line Fruit_s Nb_eggs Nb_adults SA Emergence_rate
## 993 Cherry CE1 GF 76 6 0 0.07894737
## 994 Cherry CE1 GF 89 17 0 0.19101124
## 995 Cherry CE1 GF 57 12 0 0.21052632
## 996 Cherry CE1 GF 172 24 0 0.13953488
## 997 Cherry CE1 GF 173 33 0 0.19075145
## 998 Cherry CE1 GF 91 18 0 0.19780220head(data_G7)## Treatment Line Fruit_s Nb_eggs Nb_adults SA Emergence_rate
## 3 Strawberry CR4 Cranberry 152 68 0 0.4473684
## 4 Cranberry CR4 Cranberry 246 25 1 0.1016260
## 5 Cherry CR4 Cranberry 238 29 0 0.1218487
## 6 Cherry CR4 Cranberry 166 23 0 0.1385542
## 8 Cranberry FR3 Strawberry 204 5 0 0.0245098
## 9 Strawberry FR3 Strawberry 124 45 1 0.3629032head(data_G29)## Treatment Line Fruit_s Nb_eggs Nb_adults SA Emergence_rate
## 5392 Strawberry CEA Cherry 196 16 0 0.08163265
## 5393 Strawberry CEA Cherry 192 30 0 0.15625000
## 5394 Strawberry CEA Cherry 160 17 0 0.10625000
## 5395 Strawberry CEA Cherry 106 9 0 0.08490566
## 5396 Strawberry CEA Cherry 119 14 0 0.11764706
## 5397 Strawberry CEA Cherry 204 24 0 0.11764706dim(data_G29)## [1] 990 7## Add line variable
levels(data_G0$Line) <- rep("Anc", nlevels(data_G0$Line))
## Combine datsets
data <- rbind(data_G0, data_G7, data_G29)
data <- data.frame(Generation = c(rep("0", nrow(data_G0)), rep("7", nrow(data_G7)), rep("29", nrow(data_G29))), data, Obs= as.factor(1:nrow(data)))
head(data)## Generation Treatment Line Fruit_s Nb_eggs Nb_adults SA Emergence_rate Obs
## 993 0 Cherry Anc GF 76 6 0 0.07894737 1
## 994 0 Cherry Anc GF 89 17 0 0.19101124 2
## 995 0 Cherry Anc GF 57 12 0 0.21052632 3
## 996 0 Cherry Anc GF 172 24 0 0.13953488 4
## 997 0 Cherry Anc GF 173 33 0 0.19075145 5
## 998 0 Cherry Anc GF 91 18 0 0.19780220 6## New variable for analyses
data$Generation_Fruit_s_Treatment <- as.factor(paste(data$Generation, data$Fruit_s, data$Treatment, sep="_"))
data$Line_Treatment <- as.factor(paste(data$Line, data$Treatment, sep="_"))
data$Treatmentrel <- relevel(data$Treatment, ref="Strawberry")1.2 Compute fitess change per population
1.2.1 Small example with a plot of the likelihod difference as a function of each parameter value
mfitness <- MASS::glm.nb(Nb_adults ~ Treatment, data=data)
summary(mfitness)##
## Call:
## MASS::glm.nb(formula = Nb_adults ~ Treatment, data = data, init.theta = 1.909520807,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.2900 -0.8197 -0.1146 0.4139 2.7450
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.39580 0.03163 107.365 <2e-16 ***
## TreatmentCranberry -0.05073 0.04470 -1.135 0.256
## TreatmentStrawberry 0.02466 0.04471 0.552 0.581
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(1.9095) family taken to be 1)
##
## Null deviance: 1892.5 on 1673 degrees of freedom
## Residual deviance: 1889.5 on 1671 degrees of freedom
## AIC: 14468
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 1.9095
## Std. Err.: 0.0698
##
## 2 x log-likelihood: -14460.0940pr1 <- profile(mfitness, alpha = 0.1)
MASS:::plot.profile(pr1)
1.2.2 Fitness
mfitness <- MASS::glm.nb(Nb_adults ~ -1 + Treatment + Line:Treatment, data=data)
summary(mfitness)##
## Call:
## MASS::glm.nb(formula = Nb_adults ~ -1 + Treatment + Line:Treatment,
## data = data, init.theta = 2.366102685, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.8419 -0.7673 -0.0402 0.4835 3.7740
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## TreatmentCherry 3.21848 0.06802 47.318 < 2e-16 ***
## TreatmentCranberry 3.26385 0.06789 48.077 < 2e-16 ***
## TreatmentStrawberry 3.35446 0.06764 49.590 < 2e-16 ***
## TreatmentCherry:LineCE1 -0.27404 0.22837 -1.200 0.230153
## TreatmentCranberry:LineCE1 -0.91247 0.26333 -3.465 0.000530 ***
## TreatmentStrawberry:LineCE1 0.09553 0.24774 0.386 0.699777
## TreatmentCherry:LineCE2 -0.56172 0.35753 -1.571 0.116160
## TreatmentCranberry:LineCE2 -0.20358 0.40139 -0.507 0.612024
## TreatmentStrawberry:LineCE2 0.22906 0.47936 0.478 0.632752
## TreatmentCherry:LineCE3 0.18528 0.19923 0.930 0.352387
## TreatmentCranberry:LineCE3 -0.11040 0.20830 -0.530 0.596097
## TreatmentStrawberry:LineCE3 -0.01404 0.19953 -0.070 0.943891
## TreatmentCherry:LineCE4 0.12791 0.31015 0.412 0.680033
## TreatmentCranberry:LineCE4 0.46905 0.30646 1.531 0.125891
## TreatmentStrawberry:LineCE4 0.58713 0.33925 1.731 0.083510 .
## TreatmentCherry:LineCR1 0.63167 0.66973 0.943 0.345592
## TreatmentCranberry:LineCR1 0.26984 0.34288 0.787 0.431298
## TreatmentStrawberry:LineCR1 -0.46408 0.49363 -0.940 0.347144
## TreatmentCherry:LineCR2 0.37884 0.34225 1.107 0.268337
## TreatmentCranberry:LineCR2 -0.10685 0.28660 -0.373 0.709287
## TreatmentStrawberry:LineCR2 0.23598 0.34224 0.690 0.490496
## TreatmentCherry:LineCR3 -0.62165 0.17548 -3.543 0.000396 ***
## TreatmentCranberry:LineCR3 0.05810 0.16264 0.357 0.720928
## TreatmentStrawberry:LineCR3 0.10658 0.16516 0.645 0.518711
## TreatmentCherry:LineCR4 0.20904 0.30927 0.676 0.499096
## TreatmentCranberry:LineCR4 0.35156 0.28201 1.247 0.212540
## TreatmentStrawberry:LineCR4 0.52917 0.30532 1.733 0.083064 .
## TreatmentCherry:LineCR5 0.53338 0.30635 1.741 0.081667 .
## TreatmentCranberry:LineCR5 0.18840 0.26365 0.715 0.474855
## TreatmentStrawberry:LineCR5 0.10344 0.34367 0.301 0.763430
## TreatmentCherry:LineFR1 0.34257 0.14022 2.443 0.014561 *
## TreatmentCranberry:LineFR1 0.35172 0.14181 2.480 0.013131 *
## TreatmentStrawberry:LineFR1 0.33051 0.13626 2.426 0.015285 *
## TreatmentCherry:LineFR2 0.35574 0.28238 1.260 0.207744
## TreatmentCranberry:LineFR2 -0.16526 0.28734 -0.575 0.565206
## TreatmentStrawberry:LineFR2 0.09023 0.28343 0.318 0.750229
## TreatmentCherry:LineFR3 0.57926 0.30600 1.893 0.058361 .
## TreatmentCranberry:LineFR3 0.17014 0.30917 0.550 0.582113
## TreatmentStrawberry:LineFR3 0.13336 0.26329 0.507 0.612498
## TreatmentCherry:LineFR4 0.72973 0.16699 4.370 1.24e-05 ***
## TreatmentCranberry:LineFR4 0.36284 0.17617 2.060 0.039432 *
## TreatmentStrawberry:LineFR4 0.58063 0.17071 3.401 0.000671 ***
## TreatmentCherry:LineFR5 0.30788 0.67578 0.456 0.648677
## TreatmentCranberry:LineFR5 0.39971 0.67297 0.594 0.552542
## TreatmentStrawberry:LineFR5 0.18650 0.47999 0.389 0.697600
## TreatmentCherry:LineCEA 0.14767 0.14094 1.048 0.294766
## TreatmentCranberry:LineCEA -0.21774 0.14241 -1.529 0.126279
## TreatmentStrawberry:LineCEA 0.01971 0.14073 0.140 0.888596
## TreatmentCherry:LineCEB 0.25038 0.14054 1.781 0.074831 .
## TreatmentCranberry:LineCEB -0.59200 0.14492 -4.085 4.41e-05 ***
## TreatmentStrawberry:LineCEB -0.15851 0.14152 -1.120 0.262699
## TreatmentCherry:LineCEC 0.35854 0.14017 2.558 0.010528 *
## TreatmentCranberry:LineCEC -0.20671 0.14235 -1.452 0.146475
## TreatmentStrawberry:LineCEC 0.21131 0.14002 1.509 0.131258
## TreatmentCherry:LineCRA 0.32538 0.14028 2.320 0.020366 *
## TreatmentCranberry:LineCRA -0.14885 0.14204 -1.048 0.294660
## TreatmentStrawberry:LineCRA -0.29731 0.14224 -2.090 0.036591 *
## TreatmentCherry:LineCRB 0.16930 0.14086 1.202 0.229393
## TreatmentCranberry:LineCRB 0.29720 0.14016 2.120 0.033966 *
## TreatmentStrawberry:LineCRB -0.24539 0.14196 -1.729 0.083870 .
## TreatmentCherry:LineCRC 0.08474 0.14121 0.600 0.548430
## TreatmentCranberry:LineCRC 0.02704 0.14120 0.191 0.848148
## TreatmentStrawberry:LineCRC -0.28951 0.14219 -2.036 0.041748 *
## TreatmentCherry:LineCRD 0.54736 0.13959 3.921 8.81e-05 ***
## TreatmentCranberry:LineCRD -0.15628 0.14208 -1.100 0.271367
## TreatmentStrawberry:LineCRD -1.02555 0.14802 -6.928 4.26e-12 ***
## TreatmentCherry:LineCRE -0.10496 0.14211 -0.739 0.460165
## TreatmentCranberry:LineCRE 0.72513 0.13897 5.218 1.81e-07 ***
## TreatmentStrawberry:LineCRE -0.16810 0.14157 -1.187 0.235050
## TreatmentCherry:LineFRA -0.50599 0.14466 -3.498 0.000469 ***
## TreatmentCranberry:LineFRA -0.25159 0.14260 -1.764 0.077692 .
## TreatmentStrawberry:LineFRA 0.15709 0.14021 1.120 0.262542
## TreatmentCherry:LineFRB 0.19377 0.14076 1.377 0.168632
## TreatmentCranberry:LineFRB 0.04304 0.14113 0.305 0.760404
## TreatmentStrawberry:LineFRB 0.56023 0.13903 4.030 5.59e-05 ***
## TreatmentCherry:LineFRC 0.32152 0.14029 2.292 0.021918 *
## TreatmentCranberry:LineFRC 0.52110 0.13948 3.736 0.000187 ***
## TreatmentStrawberry:LineFRC 0.36641 0.13954 2.626 0.008643 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(2.3661) family taken to be 1)
##
## Null deviance: 97071.5 on 1674 degrees of freedom
## Residual deviance: 1897.6 on 1596 degrees of freedom
## AIC: 14287
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 2.3661
## Std. Err.: 0.0906
##
## 2 x log-likelihood: -14129.0600CIfitness <- confint(mfitness)
pr1 <- profile(mfitness, alpha = 0.1)
# pdf(file="figures/Profile.pdf")
# MASS:::plot.profile(pr1)
# dev.off()1.2.3 Fecundity
mfecundity <- MASS::glm.nb(Nb_eggs ~ -1+Treatment+Line:Treatment, data=data)
summary(mfecundity)##
## Call:
## MASS::glm.nb(formula = Nb_eggs ~ -1 + Treatment + Line:Treatment,
## data = data, init.theta = 8.2113987, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -5.7949 -0.6864 -0.0232 0.5593 3.0051
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## TreatmentCherry 5.00723 0.03584 139.699 < 2e-16 ***
## TreatmentCranberry 4.81096 0.03604 133.472 < 2e-16 ***
## TreatmentStrawberry 4.68065 0.03620 129.294 < 2e-16 ***
## TreatmentCherry:LineCE1 0.21744 0.11833 1.838 0.066115 .
## TreatmentCranberry:LineCE1 0.39304 0.13118 2.996 0.002734 **
## TreatmentStrawberry:LineCE1 0.48342 0.13133 3.681 0.000232 ***
## TreatmentCherry:LineCE2 0.55345 0.18081 3.061 0.002206 **
## TreatmentCranberry:LineCE2 0.43255 0.20893 2.070 0.038430 *
## TreatmentStrawberry:LineCE2 0.27518 0.25636 1.073 0.283097
## TreatmentCherry:LineCE3 0.18914 0.10525 1.797 0.072338 .
## TreatmentCranberry:LineCE3 0.33605 0.10924 3.076 0.002096 **
## TreatmentStrawberry:LineCE3 0.43781 0.10554 4.148 3.35e-05 ***
## TreatmentCherry:LineCE4 0.29906 0.16320 1.832 0.066879 .
## TreatmentCranberry:LineCE4 0.27910 0.16397 1.702 0.088737 .
## TreatmentStrawberry:LineCE4 0.69925 0.18141 3.855 0.000116 ***
## TreatmentCherry:LineCR1 -0.27103 0.36310 -0.746 0.455399
## TreatmentCranberry:LineCR1 0.07562 0.18339 0.412 0.680090
## TreatmentStrawberry:LineCR1 0.23568 0.25664 0.918 0.358456
## TreatmentCherry:LineCR2 0.17033 0.18205 0.936 0.349452
## TreatmentCranberry:LineCR2 0.24740 0.15052 1.644 0.100242
## TreatmentStrawberry:LineCR2 0.19455 0.18348 1.060 0.288993
## TreatmentCherry:LineCR3 0.28872 0.08921 3.236 0.001210 **
## TreatmentCranberry:LineCR3 0.44703 0.08571 5.216 1.83e-07 ***
## TreatmentStrawberry:LineCR3 0.34126 0.08792 3.882 0.000104 ***
## TreatmentCherry:LineCR4 0.16779 0.16362 1.025 0.305142
## TreatmentCranberry:LineCR4 0.48150 0.14978 3.215 0.001306 **
## TreatmentStrawberry:LineCR4 0.32330 0.16434 1.967 0.049161 *
## TreatmentCherry:LineCR5 0.08652 0.16391 0.528 0.597611
## TreatmentCranberry:LineCR5 0.38990 0.13959 2.793 0.005218 **
## TreatmentStrawberry:LineCR5 0.25562 0.18317 1.396 0.162852
## TreatmentCherry:LineFR1 0.28390 0.07424 3.824 0.000131 ***
## TreatmentCranberry:LineFR1 0.37275 0.07544 4.941 7.78e-07 ***
## TreatmentStrawberry:LineFR1 0.51854 0.07272 7.130 1.00e-12 ***
## TreatmentCherry:LineFR2 0.05431 0.15046 0.361 0.718126
## TreatmentCranberry:LineFR2 0.52657 0.14966 3.519 0.000434 ***
## TreatmentStrawberry:LineFR2 0.27283 0.15094 1.807 0.070687 .
## TreatmentCherry:LineFR3 0.05916 0.16402 0.361 0.718350
## TreatmentCranberry:LineFR3 0.31419 0.16384 1.918 0.055159 .
## TreatmentStrawberry:LineFR3 0.30784 0.14029 2.194 0.028215 *
## TreatmentCherry:LineFR4 0.15635 0.08942 1.749 0.080364 .
## TreatmentCranberry:LineFR4 0.56758 0.09346 6.073 1.25e-09 ***
## TreatmentStrawberry:LineFR4 0.50984 0.09157 5.568 2.58e-08 ***
## TreatmentCherry:LineFR5 -0.04438 0.36064 -0.123 0.902049
## TreatmentCranberry:LineFR5 0.93843 0.35534 2.641 0.008268 **
## TreatmentStrawberry:LineFR5 0.67594 0.25409 2.660 0.007808 **
## TreatmentCherry:LineCEA -0.33378 0.07520 -4.438 9.06e-06 ***
## TreatmentCranberry:LineCEA 0.05785 0.07493 0.772 0.440078
## TreatmentStrawberry:LineCEA 0.25933 0.07489 3.463 0.000535 ***
## TreatmentCherry:LineCEB -0.38291 0.07531 -5.085 3.68e-07 ***
## TreatmentCranberry:LineCEB -0.08653 0.07520 -1.151 0.249844
## TreatmentStrawberry:LineCEB 0.14071 0.07509 1.874 0.060950 .
## TreatmentCherry:LineCEC -0.37932 0.07530 -5.038 4.72e-07 ***
## TreatmentCranberry:LineCEC 0.06855 0.07491 0.915 0.360163
## TreatmentStrawberry:LineCEC 0.18483 0.07501 2.464 0.013739 *
## TreatmentCherry:LineCRA -0.31070 0.07516 -4.134 3.56e-05 ***
## TreatmentCranberry:LineCRA -0.15067 0.07533 -2.000 0.045469 *
## TreatmentStrawberry:LineCRA 0.16825 0.07504 2.242 0.024953 *
## TreatmentCherry:LineCRB -0.37347 0.07529 -4.961 7.02e-07 ***
## TreatmentCranberry:LineCRB 0.10950 0.07485 1.463 0.143450
## TreatmentStrawberry:LineCRB 0.19988 0.07499 2.666 0.007687 **
## TreatmentCherry:LineCRC -0.24420 0.07503 -3.255 0.001134 **
## TreatmentCranberry:LineCRC -0.07155 0.07517 -0.952 0.341128
## TreatmentStrawberry:LineCRC 0.18174 0.07502 2.423 0.015406 *
## TreatmentCherry:LineCRD -0.26201 0.07506 -3.491 0.000482 ***
## TreatmentCranberry:LineCRD 0.10853 0.07485 1.450 0.147050
## TreatmentStrawberry:LineCRD -0.03786 0.07544 -0.502 0.615747
## TreatmentCherry:LineCRE -0.33783 0.07521 -4.492 7.06e-06 ***
## TreatmentCranberry:LineCRE 0.01307 0.07501 0.174 0.861633
## TreatmentStrawberry:LineCRE 0.18226 0.07502 2.430 0.015116 *
## TreatmentCherry:LineFRA -0.34947 0.07524 -4.645 3.40e-06 ***
## TreatmentCranberry:LineFRA -0.06923 0.07516 -0.921 0.357045
## TreatmentStrawberry:LineFRA 0.12446 0.07512 1.657 0.097540 .
## TreatmentCherry:LineFRB -0.49820 0.07557 -6.592 4.33e-11 ***
## TreatmentCranberry:LineFRB -0.20679 0.07545 -2.741 0.006127 **
## TreatmentStrawberry:LineFRB 0.14232 0.07509 1.895 0.058043 .
## TreatmentCherry:LineFRC -0.58678 0.07580 -7.742 9.82e-15 ***
## TreatmentCranberry:LineFRC -0.10565 0.07523 -1.404 0.160247
## TreatmentStrawberry:LineFRC 0.21520 0.07496 2.871 0.004094 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(8.2114) family taken to be 1)
##
## Null deviance: 889732.5 on 1674 degrees of freedom
## Residual deviance: 1730.1 on 1596 degrees of freedom
## AIC: 17660
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 8.211
## Std. Err.: 0.301
##
## 2 x log-likelihood: -17501.650CIfecundity <- confint(mfecundity)1.2.4 Egg-to-adult viability
## 4 tubes with more adults than eggs
sum(data$Nb_adults>data$Nb_eggs)## [1] 4## Number of adults=number of eggs
data$Nb_eggs[data$Nb_adults>data$Nb_eggs] <- data$Nb_adults[data$Nb_adults>data$Nb_eggs]
## Fit model
megg_to_ad <- glm(cbind(Nb_adults, Nb_eggs) ~ -1+Treatment+Line:Treatment, data=data, family="binomial")
summary(megg_to_ad)##
## Call:
## glm(formula = cbind(Nb_adults, Nb_eggs) ~ -1 + Treatment + Line:Treatment,
## family = "binomial", data = data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -11.2550 -2.2309 0.0851 2.0910 13.2204
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## TreatmentCherry -1.788754 0.021611 -82.769 < 2e-16 ***
## TreatmentCranberry -1.547115 0.021536 -71.838 < 2e-16 ***
## TreatmentStrawberry -1.326194 0.021024 -63.080 < 2e-16 ***
## TreatmentCherry:LineCE1 -0.494166 0.079164 -6.242 4.31e-10 ***
## TreatmentCranberry:LineCE1 -1.305517 0.114258 -11.426 < 2e-16 ***
## TreatmentStrawberry:LineCE1 -0.387890 0.071590 -5.418 6.02e-08 ***
## TreatmentCherry:LineCE2 -1.115171 0.137737 -8.096 5.66e-16 ***
## TreatmentCranberry:LineCE2 -0.636123 0.133602 -4.761 1.92e-06 ***
## TreatmentStrawberry:LineCE2 -0.046115 0.133611 -0.345 0.729990
## TreatmentCherry:LineCE3 -0.003858 0.058741 -0.066 0.947636
## TreatmentCranberry:LineCE3 -0.446447 0.067136 -6.650 2.93e-11 ***
## TreatmentStrawberry:LineCE3 -0.451848 0.060226 -7.503 6.26e-14 ***
## TreatmentCherry:LineCE4 -0.171143 0.092203 -1.856 0.063432 .
## TreatmentCranberry:LineCE4 0.189949 0.080499 2.360 0.018292 *
## TreatmentStrawberry:LineCE4 -0.112122 0.080302 -1.396 0.162639
## TreatmentCherry:LineCR1 0.902703 0.174687 5.168 2.37e-07 ***
## TreatmentCranberry:LineCR1 0.194219 0.098234 1.977 0.048029 *
## TreatmentStrawberry:LineCR1 -0.699759 0.178558 -3.919 8.89e-05 ***
## TreatmentCherry:LineCR2 0.208505 0.093417 2.232 0.025617 *
## TreatmentCranberry:LineCR2 -0.354251 0.092819 -3.817 0.000135 ***
## TreatmentStrawberry:LineCR2 0.041436 0.096161 0.431 0.666543
## TreatmentCherry:LineCR3 -0.910368 0.068209 -13.347 < 2e-16 ***
## TreatmentCranberry:LineCR3 -0.388928 0.049294 -7.890 3.02e-15 ***
## TreatmentStrawberry:LineCR3 -0.234674 0.048389 -4.850 1.24e-06 ***
## TreatmentCherry:LineCR4 0.041249 0.089954 0.459 0.646551
## TreatmentCranberry:LineCR4 -0.129940 0.076068 -1.708 0.087598 .
## TreatmentStrawberry:LineCR4 0.205871 0.076808 2.680 0.007355 **
## TreatmentCherry:LineCR5 0.446858 0.079930 5.591 2.26e-08 ***
## TreatmentCranberry:LineCR5 -0.201495 0.076001 -2.651 0.008020 **
## TreatmentStrawberry:LineCR5 -0.152185 0.100555 -1.513 0.130167
## TreatmentCherry:LineFR1 0.058675 0.039773 1.475 0.140147
## TreatmentCranberry:LineFR1 -0.021037 0.039810 -0.528 0.597192
## TreatmentStrawberry:LineFR1 -0.188028 0.037401 -5.027 4.97e-07 ***
## TreatmentCherry:LineFR2 0.301431 0.078714 3.829 0.000128 ***
## TreatmentCranberry:LineFR2 -0.691834 0.093722 -7.382 1.56e-13 ***
## TreatmentStrawberry:LineFR2 -0.197779 0.083186 -2.378 0.017428 *
## TreatmentCherry:LineFR3 0.520102 0.078819 6.599 4.15e-11 ***
## TreatmentCranberry:LineFR3 -0.144052 0.090025 -1.600 0.109570
## TreatmentStrawberry:LineFR3 -0.174482 0.076043 -2.295 0.021761 *
## TreatmentCherry:LineFR4 0.573374 0.042230 13.577 < 2e-16 ***
## TreatmentCranberry:LineFR4 -0.204735 0.047960 -4.269 1.96e-05 ***
## TreatmentStrawberry:LineFR4 0.070797 0.042863 1.652 0.098590 .
## TreatmentCherry:LineFR5 0.352270 0.192020 1.835 0.066573 .
## TreatmentCranberry:LineFR5 -0.538716 0.171142 -3.148 0.001645 **
## TreatmentStrawberry:LineFR5 -0.489433 0.131504 -3.722 0.000198 ***
## TreatmentCherry:LineCEA 0.481448 0.043922 10.961 < 2e-16 ***
## TreatmentCranberry:LineCEA -0.275592 0.048007 -5.741 9.43e-09 ***
## TreatmentStrawberry:LineCEA -0.239612 0.042686 -5.613 1.98e-08 ***
## TreatmentCherry:LineCEB 0.633291 0.042808 14.794 < 2e-16 ***
## TreatmentCranberry:LineCEB -0.505472 0.055352 -9.132 < 2e-16 ***
## TreatmentStrawberry:LineCEB -0.299214 0.045550 -6.569 5.07e-11 ***
## TreatmentCherry:LineCEC 0.737860 0.041531 17.766 < 2e-16 ***
## TreatmentCranberry:LineCEC -0.275258 0.047797 -5.759 8.47e-09 ***
## TreatmentStrawberry:LineCEC 0.026482 0.040515 0.654 0.513343
## TreatmentCherry:LineCRA 0.636074 0.041649 15.272 < 2e-16 ***
## TreatmentCranberry:LineCRA 0.001821 0.047524 0.038 0.969433
## TreatmentStrawberry:LineCRA -0.465566 0.047651 -9.770 < 2e-16 ***
## TreatmentCherry:LineCRB 0.542770 0.043785 12.396 < 2e-16 ***
## TreatmentCranberry:LineCRB 0.187694 0.040669 4.615 3.93e-06 ***
## TreatmentStrawberry:LineCRB -0.445272 0.046725 -9.530 < 2e-16 ***
## TreatmentCherry:LineCRC 0.328942 0.044466 7.398 1.39e-13 ***
## TreatmentCranberry:LineCRC 0.098592 0.044676 2.207 0.027328 *
## TreatmentStrawberry:LineCRC -0.471252 0.047486 -9.924 < 2e-16 ***
## TreatmentCherry:LineCRD 0.809372 0.039095 20.703 < 2e-16 ***
## TreatmentCranberry:LineCRD -0.264808 0.046878 -5.649 1.62e-08 ***
## TreatmentStrawberry:LineCRD -0.987691 0.063323 -15.598 < 2e-16 ***
## TreatmentCherry:LineCRE 0.232873 0.047551 4.897 9.72e-07 ***
## TreatmentCranberry:LineCRE 0.712061 0.036727 19.388 < 2e-16 ***
## TreatmentStrawberry:LineCRE -0.350362 0.045574 -7.688 1.50e-14 ***
## TreatmentCherry:LineFRA -0.156524 0.054733 -2.860 0.004239 **
## TreatmentCranberry:LineFRA -0.182362 0.048928 -3.727 0.000194 ***
## TreatmentStrawberry:LineFRA 0.032626 0.041352 0.789 0.430124
## TreatmentCherry:LineFRB 0.691975 0.043965 15.739 < 2e-16 ***
## TreatmentCranberry:LineFRB 0.249832 0.044927 5.561 2.69e-08 ***
## TreatmentStrawberry:LineFRB 0.407777 0.037045 11.008 < 2e-16 ***
## TreatmentCherry:LineFRC 0.908301 0.042838 21.203 < 2e-16 ***
## TreatmentCranberry:LineFRC 0.626745 0.039018 16.063 < 2e-16 ***
## TreatmentStrawberry:LineFRC 0.151208 0.038709 3.906 9.37e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 142163 on 1674 degrees of freedom
## Residual deviance: 17859 on 1596 degrees of freedom
## AIC: 25827
##
## Number of Fisher Scoring iterations: 5CImegg_to_ad <- confint(megg_to_ad)1.3 Build data_logchange dataset
## Extract all CIs
## J'ai garder le nom logchange pour que le code pour la Fig. 4 marche, mais je pense que ça vaudrait le coup que le nom soit plus informatif, comme logfitnesschange par exemple
## Check that the coef are in the same order
identical(names(coef(mfitness)), names(coef(mfecundity)))## [1] TRUEidentical(names(coef(mfitness)), names(coef(megg_to_ad)))## [1] TRUEdata_logchange <- data.frame(logchange=coef(mfitness),
lowCIlogfitnesschange=CIfitness[, 1],
upCIlogfitnesschange=CIfitness[, 2],
logfecundchange=coef(mfecundity),
lowCIlogfecundchange=CIfecundity[, 1],
upCIlogfecundchange=CIfecundity[, 2],
logeggtoadchange=coef(megg_to_ad),
lowCIlogeggtoadchange=CImegg_to_ad[, 1],
upCIlogeggtoadchange=CImegg_to_ad[, 2])
head(data_logchange)## logchange lowCIlogfitnesschange
## TreatmentCherry 3.21847574 3.0875555
## TreatmentCranberry 3.26384919 3.1331965
## TreatmentStrawberry 3.35445512 3.2243029
## TreatmentCherry:LineCE1 -0.27403677 -0.7021017
## TreatmentCranberry:LineCE1 -0.91247393 -1.4069620
## TreatmentStrawberry:LineCE1 0.09553243 -0.3637574
## upCIlogfitnesschange logfecundchange
## TreatmentCherry 3.3543047 5.0072295
## TreatmentCranberry 3.3994333 4.8109641
## TreatmentStrawberry 3.4895811 4.6806486
## TreatmentCherry:LineCE1 0.1969999 0.2174413
## TreatmentCranberry:LineCE1 -0.3690326 0.3930426
## TreatmentStrawberry:LineCE1 0.6127366 0.4834228
## lowCIlogfecundchange upCIlogfecundchange
## TreatmentCherry 4.937707676 5.0782276
## TreatmentCranberry 4.741037413 4.8823484
## TreatmentStrawberry 4.610407277 4.7523331
## TreatmentCherry:LineCE1 -0.008054728 0.4563722
## TreatmentCranberry:LineCE1 0.144087552 0.6591387
## TreatmentStrawberry:LineCE1 0.234153348 0.7497917
## logeggtoadchange lowCIlogeggtoadchange
## TreatmentCherry -1.7887538 -1.8313270
## TreatmentCranberry -1.5471149 -1.5895200
## TreatmentStrawberry -1.3261935 -1.3675662
## TreatmentCherry:LineCE1 -0.4941655 -0.6522000
## TreatmentCranberry:LineCE1 -1.3055165 -1.5366802
## TreatmentStrawberry:LineCE1 -0.3878904 -0.5301533
## upCIlogeggtoadchange
## TreatmentCherry -1.7466080
## TreatmentCranberry -1.5050959
## TreatmentStrawberry -1.2851499
## TreatmentCherry:LineCE1 -0.3417120
## TreatmentCranberry:LineCE1 -1.0882241
## TreatmentStrawberry:LineCE1 -0.2494137## Remove estimates from ancestral population
data_logchange <- data_logchange[4:nrow(data_logchange), ]
## Extract information regarding line, selective and test media
rowname <- strsplit(rownames(data_logchange), split=":")
Treatment <- as.factor(gsub("Treatment", "", lapply(rowname, `[[`, 1)))
Generation <- Line <- as.factor(gsub("Line", "", lapply(rowname, `[[`, 2)))
## Change Fruit_s levels
Fruit_s <- as.factor(substr(Line, 1, 2))
levels(Fruit_s) <- levels(Treatment)
## Change levels
levels(Generation) <- ifelse(is.na(as.numeric(substr(levels(Generation), 3, 3))), "29", "7")
## Combine data
data_info <- data.frame(Treatment, Line, Fruit_s, Generation)
data_info$Line_Treatement <- paste(data_info$Line, data_info$Treatment, sep="_")
# Add symp and allop
data_info$SA <- as.factor(ifelse(data_info$Treatment == data_info$Fruit_s , 1, 0))
data.frame(names(coef(mfitness))[4:length(coef(mfitness))], data_info)## names.coef.mfitness...4.length.coef.mfitness... Treatment Line Fruit_s
## 1 TreatmentCherry:LineCE1 Cherry CE1 Cherry
## 2 TreatmentCranberry:LineCE1 Cranberry CE1 Cherry
## 3 TreatmentStrawberry:LineCE1 Strawberry CE1 Cherry
## 4 TreatmentCherry:LineCE2 Cherry CE2 Cherry
## 5 TreatmentCranberry:LineCE2 Cranberry CE2 Cherry
## 6 TreatmentStrawberry:LineCE2 Strawberry CE2 Cherry
## 7 TreatmentCherry:LineCE3 Cherry CE3 Cherry
## 8 TreatmentCranberry:LineCE3 Cranberry CE3 Cherry
## 9 TreatmentStrawberry:LineCE3 Strawberry CE3 Cherry
## 10 TreatmentCherry:LineCE4 Cherry CE4 Cherry
## 11 TreatmentCranberry:LineCE4 Cranberry CE4 Cherry
## 12 TreatmentStrawberry:LineCE4 Strawberry CE4 Cherry
## 13 TreatmentCherry:LineCR1 Cherry CR1 Cranberry
## 14 TreatmentCranberry:LineCR1 Cranberry CR1 Cranberry
## 15 TreatmentStrawberry:LineCR1 Strawberry CR1 Cranberry
## 16 TreatmentCherry:LineCR2 Cherry CR2 Cranberry
## 17 TreatmentCranberry:LineCR2 Cranberry CR2 Cranberry
## 18 TreatmentStrawberry:LineCR2 Strawberry CR2 Cranberry
## 19 TreatmentCherry:LineCR3 Cherry CR3 Cranberry
## 20 TreatmentCranberry:LineCR3 Cranberry CR3 Cranberry
## 21 TreatmentStrawberry:LineCR3 Strawberry CR3 Cranberry
## 22 TreatmentCherry:LineCR4 Cherry CR4 Cranberry
## 23 TreatmentCranberry:LineCR4 Cranberry CR4 Cranberry
## 24 TreatmentStrawberry:LineCR4 Strawberry CR4 Cranberry
## 25 TreatmentCherry:LineCR5 Cherry CR5 Cranberry
## 26 TreatmentCranberry:LineCR5 Cranberry CR5 Cranberry
## 27 TreatmentStrawberry:LineCR5 Strawberry CR5 Cranberry
## 28 TreatmentCherry:LineFR1 Cherry FR1 Strawberry
## 29 TreatmentCranberry:LineFR1 Cranberry FR1 Strawberry
## 30 TreatmentStrawberry:LineFR1 Strawberry FR1 Strawberry
## 31 TreatmentCherry:LineFR2 Cherry FR2 Strawberry
## 32 TreatmentCranberry:LineFR2 Cranberry FR2 Strawberry
## 33 TreatmentStrawberry:LineFR2 Strawberry FR2 Strawberry
## 34 TreatmentCherry:LineFR3 Cherry FR3 Strawberry
## 35 TreatmentCranberry:LineFR3 Cranberry FR3 Strawberry
## 36 TreatmentStrawberry:LineFR3 Strawberry FR3 Strawberry
## 37 TreatmentCherry:LineFR4 Cherry FR4 Strawberry
## 38 TreatmentCranberry:LineFR4 Cranberry FR4 Strawberry
## 39 TreatmentStrawberry:LineFR4 Strawberry FR4 Strawberry
## 40 TreatmentCherry:LineFR5 Cherry FR5 Strawberry
## 41 TreatmentCranberry:LineFR5 Cranberry FR5 Strawberry
## 42 TreatmentStrawberry:LineFR5 Strawberry FR5 Strawberry
## 43 TreatmentCherry:LineCEA Cherry CEA Cherry
## 44 TreatmentCranberry:LineCEA Cranberry CEA Cherry
## 45 TreatmentStrawberry:LineCEA Strawberry CEA Cherry
## 46 TreatmentCherry:LineCEB Cherry CEB Cherry
## 47 TreatmentCranberry:LineCEB Cranberry CEB Cherry
## 48 TreatmentStrawberry:LineCEB Strawberry CEB Cherry
## 49 TreatmentCherry:LineCEC Cherry CEC Cherry
## 50 TreatmentCranberry:LineCEC Cranberry CEC Cherry
## 51 TreatmentStrawberry:LineCEC Strawberry CEC Cherry
## 52 TreatmentCherry:LineCRA Cherry CRA Cranberry
## 53 TreatmentCranberry:LineCRA Cranberry CRA Cranberry
## 54 TreatmentStrawberry:LineCRA Strawberry CRA Cranberry
## 55 TreatmentCherry:LineCRB Cherry CRB Cranberry
## 56 TreatmentCranberry:LineCRB Cranberry CRB Cranberry
## 57 TreatmentStrawberry:LineCRB Strawberry CRB Cranberry
## 58 TreatmentCherry:LineCRC Cherry CRC Cranberry
## 59 TreatmentCranberry:LineCRC Cranberry CRC Cranberry
## 60 TreatmentStrawberry:LineCRC Strawberry CRC Cranberry
## 61 TreatmentCherry:LineCRD Cherry CRD Cranberry
## 62 TreatmentCranberry:LineCRD Cranberry CRD Cranberry
## 63 TreatmentStrawberry:LineCRD Strawberry CRD Cranberry
## 64 TreatmentCherry:LineCRE Cherry CRE Cranberry
## 65 TreatmentCranberry:LineCRE Cranberry CRE Cranberry
## 66 TreatmentStrawberry:LineCRE Strawberry CRE Cranberry
## 67 TreatmentCherry:LineFRA Cherry FRA Strawberry
## 68 TreatmentCranberry:LineFRA Cranberry FRA Strawberry
## 69 TreatmentStrawberry:LineFRA Strawberry FRA Strawberry
## 70 TreatmentCherry:LineFRB Cherry FRB Strawberry
## 71 TreatmentCranberry:LineFRB Cranberry FRB Strawberry
## 72 TreatmentStrawberry:LineFRB Strawberry FRB Strawberry
## 73 TreatmentCherry:LineFRC Cherry FRC Strawberry
## 74 TreatmentCranberry:LineFRC Cranberry FRC Strawberry
## 75 TreatmentStrawberry:LineFRC Strawberry FRC Strawberry
## Generation Line_Treatement SA
## 1 7 CE1_Cherry 1
## 2 7 CE1_Cranberry 0
## 3 7 CE1_Strawberry 0
## 4 7 CE2_Cherry 1
## 5 7 CE2_Cranberry 0
## 6 7 CE2_Strawberry 0
## 7 7 CE3_Cherry 1
## 8 7 CE3_Cranberry 0
## 9 7 CE3_Strawberry 0
## 10 7 CE4_Cherry 1
## 11 7 CE4_Cranberry 0
## 12 7 CE4_Strawberry 0
## 13 7 CR1_Cherry 0
## 14 7 CR1_Cranberry 1
## 15 7 CR1_Strawberry 0
## 16 7 CR2_Cherry 0
## 17 7 CR2_Cranberry 1
## 18 7 CR2_Strawberry 0
## 19 7 CR3_Cherry 0
## 20 7 CR3_Cranberry 1
## 21 7 CR3_Strawberry 0
## 22 7 CR4_Cherry 0
## 23 7 CR4_Cranberry 1
## 24 7 CR4_Strawberry 0
## 25 7 CR5_Cherry 0
## 26 7 CR5_Cranberry 1
## 27 7 CR5_Strawberry 0
## 28 7 FR1_Cherry 0
## 29 7 FR1_Cranberry 0
## 30 7 FR1_Strawberry 1
## 31 7 FR2_Cherry 0
## 32 7 FR2_Cranberry 0
## 33 7 FR2_Strawberry 1
## 34 7 FR3_Cherry 0
## 35 7 FR3_Cranberry 0
## 36 7 FR3_Strawberry 1
## 37 7 FR4_Cherry 0
## 38 7 FR4_Cranberry 0
## 39 7 FR4_Strawberry 1
## 40 7 FR5_Cherry 0
## 41 7 FR5_Cranberry 0
## 42 7 FR5_Strawberry 1
## 43 29 CEA_Cherry 1
## 44 29 CEA_Cranberry 0
## 45 29 CEA_Strawberry 0
## 46 29 CEB_Cherry 1
## 47 29 CEB_Cranberry 0
## 48 29 CEB_Strawberry 0
## 49 29 CEC_Cherry 1
## 50 29 CEC_Cranberry 0
## 51 29 CEC_Strawberry 0
## 52 29 CRA_Cherry 0
## 53 29 CRA_Cranberry 1
## 54 29 CRA_Strawberry 0
## 55 29 CRB_Cherry 0
## 56 29 CRB_Cranberry 1
## 57 29 CRB_Strawberry 0
## 58 29 CRC_Cherry 0
## 59 29 CRC_Cranberry 1
## 60 29 CRC_Strawberry 0
## 61 29 CRD_Cherry 0
## 62 29 CRD_Cranberry 1
## 63 29 CRD_Strawberry 0
## 64 29 CRE_Cherry 0
## 65 29 CRE_Cranberry 1
## 66 29 CRE_Strawberry 0
## 67 29 FRA_Cherry 0
## 68 29 FRA_Cranberry 0
## 69 29 FRA_Strawberry 1
## 70 29 FRB_Cherry 0
## 71 29 FRB_Cranberry 0
## 72 29 FRB_Strawberry 1
## 73 29 FRC_Cherry 0
## 74 29 FRC_Cranberry 0
## 75 29 FRC_Strawberry 1## Compute sample size per line and test medium
sample_size <- aggregate(Nb_eggs~Line:Treatment, length, data=data[data$Generation!="0",])
sample_size$Line_Treatement <- paste(sample_size$Line, sample_size$Treatment, sep="_")
names(sample_size)[3] <- "N"
## Merge the three datasets
data_info <- merge(x=data_info, y=sample_size[, 3:4], by="Line_Treatement")
data_info$Line_Treatment <- paste0("Treatment", data_info$Treatment, ":Line", data_info$Line)
data_logchange$Line_Treatment <- rownames(data_logchange)
data_logchange <- merge(x=data_info, y=data_logchange, by = "Line_Treatment")[, -c(1, 2)]
head(data_logchange)## Treatment Line Fruit_s Generation SA N logchange lowCIlogfitnesschange
## 1 Cherry CE1 Cherry 7 1 10 -0.2740368 -0.70210174
## 2 Cherry CE2 Cherry 7 1 4 -0.5617188 -1.21534852
## 3 Cherry CE3 Cherry 7 1 13 0.1852825 -0.18979611
## 4 Cherry CE4 Cherry 7 1 5 0.1279134 -0.43781493
## 5 Cherry CEA Cherry 29 1 30 0.1476700 -0.12317382
## 6 Cherry CEB Cherry 29 1 30 0.2503803 -0.01964191
## upCIlogfitnesschange logfecundchange lowCIlogfecundchange upCIlogfecundchange
## 1 0.1969999 0.2174413 -0.008054728 0.4563722
## 2 0.2019585 0.5534521 0.216090324 0.9271423
## 3 0.5938166 0.1891403 -0.012370462 0.4006261
## 4 0.7888315 0.2990560 -0.007390043 0.6339803
## 5 0.4301216 -0.3337778 -0.479544492 -0.1846294
## 6 0.5320919 -0.3829105 -0.528887054 -0.2335638
## logeggtoadchange lowCIlogeggtoadchange upCIlogeggtoadchange
## 1 -0.494165542 -0.6522000 -0.341712049
## 2 -1.115170967 -1.3960146 -0.855075837
## 3 -0.003857865 -0.1201706 0.110148034
## 4 -0.171142635 -0.3556475 0.006047628
## 5 0.481447806 0.3949970 0.567185941
## 6 0.633290781 0.5490961 0.7169143631.4 Build sympatric/allopatric datasets
#Formatting for testing correlation
TEMP_dataG7_CheCran <- formattinglogchange(logchange_dataset = data_logchange, generation="7",
fruitcomb=c("Cherry", "Cranberry"), trait="fitness")
TEMP_dataG7_CranStraw <- formattinglogchange(logchange_dataset = data_logchange, generation="7",
fruitcomb=c("Strawberry", "Cranberry"), trait="fitness")
TEMP_dataG7_StrawChe <- formattinglogchange(logchange_dataset = data_logchange, generation="7",
fruitcomb=c("Cherry", "Strawberry"), trait="fitness")
TEMP_dataG29_CheCran <- formattinglogchange(logchange_dataset = data_logchange, generation="29",
fruitcomb=c("Cherry", "Cranberry"), trait="fitness")
TEMP_dataG29_CranStraw <- formattinglogchange(logchange_dataset = data_logchange, generation="29",
fruitcomb=c("Strawberry", "Cranberry"), trait="fitness")
TEMP_dataG29_StrawChe <- formattinglogchange(logchange_dataset = data_logchange, generation="29",
fruitcomb=c("Cherry", "Strawberry"), trait="fitness")2 ADAPTATION
2.1 Proportion of adaptation: raw data
tapply(data_sum$Nb_adults[data_sum$Generation=="2"],data_sum$Fruit_s[data_sum$Generation=="2"],mean)## Cherry Cranberry Strawberry
## 8.80000 19.38667 12.19000tapply(data_sum$Nb_adults[data_sum$Generation=="27"],data_sum$Fruit_s[data_sum$Generation=="27"],mean)## Cherry Cranberry Strawberry
## 35.84000 33.63750 42.33333## Calcul proportion change between G2 and G27
((tapply(data_sum$Nb_adults[data_sum$Generation=="27"],data_sum$Fruit_s[data_sum$Generation=="27"],mean) -
tapply(data_sum$Nb_adults[data_sum$Generation=="2"],data_sum$Fruit_s[data_sum$Generation=="2"],mean)) /
tapply(data_sum$Nb_adults[data_sum$Generation=="2"],data_sum$Fruit_s[data_sum$Generation=="2"],mean)) * 100## Cherry Cranberry Strawberry
## 307.27273 73.50843 247.27919#
# (abs(tapply(data_sum$fitness[data_sum$Generation=="27"],data_sum$Fruit_s[data_sum$Generation=="27"],mean) -
# tapply(data_sum$fitness[data_sum$Generation=="2"],data_sum$Fruit_s[data_sum$Generation=="2"],mean)) /
# abs(tapply(data_sum$fitness[data_sum$Generation=="2"],data_sum$Fruit_s[data_sum$Generation=="2"],mean))) * 100
## Calcul proportion change between G2 and phase2 (because G8 is not representative)
((tapply(data_sum$Nb_adults[data_sum$Generation=="8"],data_sum$Fruit_s[data_sum$Generation=="8"],mean) -
tapply(data_sum$Nb_adults[data_sum$Generation=="2"],data_sum$Fruit_s[data_sum$Generation=="2"],mean)) /
tapply(data_sum$Nb_adults[data_sum$Generation=="2"],data_sum$Fruit_s[data_sum$Generation=="2"],mean)) * 100## Cherry Cranberry Strawberry
## 289.61039 85.02183 89.19658## Compute proportion change between G2 and phase 2 (because G8 is not representative)
((tapply(data_sum$Nb_adults[data_sum$Phase=="pool"],data_sum$Fruit_s[data_sum$Phase=="pool"],mean) -
tapply(data_sum$Nb_adults[data_sum$Phase=="first_prepool"],data_sum$Fruit_s[data_sum$Phase=="first_prepool"],mean)) /
tapply(data_sum$Nb_adults[data_sum$Phase=="first_prepool"],data_sum$Fruit_s[data_sum$Phase=="first_prepool"],mean)) * 100## Cherry Cranberry Strawberry
## 123.10237 93.62985 45.43089((tapply(data_sum$Nb_adults[data_sum$Phase=="second_postpool"],data_sum$Fruit_s[data_sum$Phase=="second_postpool"],mean) -
tapply(data_sum$Nb_adults[data_sum$Phase=="first_prepool"],data_sum$Fruit_s[data_sum$Phase=="first_prepool"],mean)) /
tapply(data_sum$Nb_adults[data_sum$Phase=="first_prepool"],data_sum$Fruit_s[data_sum$Phase=="first_prepool"],mean)) * 100## Cherry Cranberry Strawberry
## 230.1300 176.0760 145.16092.2 Analysis
######## Models
mod1 <- lme4::lmer(fitness ~ 1 + (1|Generation:Fruit_s),
weights = N, data = data_sum, REML = FALSE)
mod2 <- lme4::lmer(fitness~ Phase + (1|Generation:Fruit_s),
weights = N, data = data_sum, REML = FALSE)
mod3 <- lme4::lmer(fitness ~ Generation +(1|Generation:Fruit_s),
weights = N, data = data_sum, REML = FALSE)
mod4 <- lme4::lmer(fitness ~ Fruit_s + (1|Generation:Fruit_s),
weights = N, data = data_sum, REML = FALSE)
mod5 <- lme4::lmer(fitness ~ Fruit_s*Phase + (1|Generation:Fruit_s),
weights = N, data = data_sum, REML = FALSE)
mod6 <- lme4::lmer(fitness ~ Fruit_s*Generation + (1|Generation:Fruit_s),
weights = N, data = data_sum, REML = FALSE)
mod7 <- lme4::lmer(fitness ~ Fruit_s + Phase + (1|Generation:Fruit_s),
weights = N, data = data_sum, REML = FALSE)
mod8 <- lme4::lmer(fitness ~ Fruit_s + Generation + (1|Generation:Fruit_s),
weights = N, data = data_sum, REML = FALSE)
MuMIn::model.sel(mod1, mod2, mod3, mod4, mod5, mod6, mod7, mod8)## Model selection table
## (Int) Phs Gnr Frt_s Frt_s:Phs Frt_s:Gnr family df
## mod7 -0.3308 + + gaussian(identity) 7
## mod2 -0.4763 + gaussian(identity) 5
## mod5 -0.5220 + + + gaussian(identity) 11
## mod8 -0.1484 0.03835 + gaussian(identity) 6
## mod3 -0.2975 0.03872 gaussian(identity) 4
## mod6 -0.3025 0.04821 + + gaussian(identity) 8
## mod1 0.2953 gaussian(identity) 3
## mod4 0.4452 + gaussian(identity) 5
## logLik AICc delta weight
## mod7 -122.683 259.8 0.00 0.811
## mod2 -126.513 263.3 3.44 0.145
## mod5 -121.280 265.7 5.85 0.044
## mod8 -137.588 287.5 27.69 0.000
## mod3 -140.167 288.5 28.67 0.000
## mod6 -136.994 290.6 30.76 0.000
## mod1 -154.997 316.1 56.26 0.000
## mod4 -153.115 316.5 56.64 0.000
## Models ranked by AICc(x)
## Random terms (all models):
## '1 | Generation:Fruit_s'summary(mod7)## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: fitness ~ Fruit_s + Phase + (1 | Generation:Fruit_s)
## Data: data_sum
## Weights: N
##
## AIC BIC logLik deviance df.resid
## 259.4 283.9 -122.7 245.4 240
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.2489 -0.3779 0.0806 0.4943 2.2620
##
## Random effects:
## Groups Name Variance Std.Dev.
## Generation:Fruit_s (Intercept) 0.07089 0.2663
## Residual 2.11535 1.4544
## Number of obs: 247, groups: Generation:Fruit_s, 72
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) -0.33076 0.11201 -2.953
## Fruit_sCranberry -0.16227 0.09128 -1.778
## Fruit_sStrawberry -0.26301 0.09342 -2.816
## Phasepool 0.57610 0.13295 4.333
## Phasesecond_postpool 0.99771 0.10710 9.316
##
## Correlation of Fixed Effects:
## (Intr) Frt_sC Frt_sS Phaspl
## Frt_sCrnbrr -0.437
## Frt_sStrwbr -0.433 0.528
## Phasepool -0.619 -0.027 -0.026
## Phsscnd_pst -0.791 0.003 0.011 0.663mod_Phase <- lme4::lmer(fitness ~ Fruit_s + Phase + (1|Generation:Fruit_s),
weights = N, data = data_sum)
#Posthoc
emmeans::emmeans(mod_Phase, list(pairwise ~ Phase), adjust = "tukey") ### $`emmeans of Phase`
## Phase emmean SE df lower.CL upper.CL
## first_prepool -0.473 0.1012 728 -0.715 -0.230
## pool 0.105 0.0942 101168 -0.120 0.330
## second_postpool 0.525 0.0469 2673 0.413 0.637
##
## Results are averaged over the levels of: Fruit_s
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
## Conf-level adjustment: sidak method for 3 estimates
##
## $`pairwise differences of Phase`
## contrast estimate SE df t.ratio p.value
## first_prepool - pool -0.577 0.138 2390 -4.177 0.0001
## first_prepool - second_postpool -0.997 0.112 866 -8.942 <.0001
## pool - second_postpool -0.420 0.105 29408 -3.993 0.0002
##
## Results are averaged over the levels of: Fruit_s
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimatesemmeans::emmeans(mod_Phase, list(pairwise ~ Phase+Fruit_s), adjust = "tukey") ### $`emmeans of Phase, Fruit_s`
## Phase Fruit_s emmean SE df lower.CL upper.CL
## first_prepool Cherry -0.3307 0.1165 960 -0.6537 -0.00774
## pool Cherry 0.2466 0.1125 21049 -0.0645 0.55772
## second_postpool Cherry 0.6666 0.0736 4340 0.4629 0.87027
## first_prepool Cranberry -0.4933 0.1139 881 -0.8090 -0.17751
## pool Cranberry 0.0840 0.1067 23944 -0.2110 0.37913
## second_postpool Cranberry 0.5040 0.0701 1279 0.3098 0.69830
## first_prepool Strawberry -0.5936 0.1151 937 -0.9127 -0.27462
## pool Strawberry -0.0163 0.1080 29146 -0.3149 0.28223
## second_postpool Strawberry 0.4037 0.0729 4392 0.2018 0.60549
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
## Conf-level adjustment: sidak method for 9 estimates
##
## $`pairwise differences of Phase, Fruit_s`
## contrast estimate SE df
## first_prepool Cherry - pool Cherry -0.577 0.1382 2390
## first_prepool Cherry - second_postpool Cherry -0.997 0.1115 866
## first_prepool Cherry - first_prepool Cranberry 0.163 0.0950 2321
## first_prepool Cherry - pool Cranberry -0.415 0.1657 2334
## first_prepool Cherry - second_postpool Cranberry -0.835 0.1469 1044
## first_prepool Cherry - first_prepool Strawberry 0.263 0.0969 4121
## first_prepool Cherry - pool Strawberry -0.314 0.1668 2829
## first_prepool Cherry - second_postpool Strawberry -0.734 0.1486 1498
## pool Cherry - second_postpool Cherry -0.420 0.1052 29408
## pool Cherry - first_prepool Cranberry 0.740 0.1697 2401
## pool Cherry - pool Cranberry 0.163 0.0950 2321
## pool Cherry - second_postpool Cranberry -0.257 0.1445 4431
## pool Cherry - first_prepool Strawberry 0.840 0.1708 2822
## pool Cherry - pool Strawberry 0.263 0.0969 4121
## pool Cherry - second_postpool Strawberry -0.157 0.1462 8921
## second_postpool Cherry - first_prepool Cranberry 1.160 0.1462 1503
## second_postpool Cherry - pool Cranberry 0.583 0.1390 11020
## second_postpool Cherry - second_postpool Cranberry 0.163 0.0950 2321
## second_postpool Cherry - first_prepool Strawberry 1.260 0.1470 1475
## second_postpool Cherry - pool Strawberry 0.683 0.1399 10394
## second_postpool Cherry - second_postpool Strawberry 0.263 0.0969 4121
## first_prepool Cranberry - pool Cranberry -0.577 0.1382 2390
## first_prepool Cranberry - second_postpool Cranberry -0.997 0.1115 866
## first_prepool Cranberry - first_prepool Strawberry 0.100 0.0935 2401
## first_prepool Cranberry - pool Strawberry -0.477 0.1668 2420
## first_prepool Cranberry - second_postpool Strawberry -0.897 0.1460 1523
## pool Cranberry - second_postpool Cranberry -0.420 0.1052 29408
## pool Cranberry - first_prepool Strawberry 0.678 0.1669 2367
## pool Cranberry - pool Strawberry 0.100 0.0935 2401
## pool Cranberry - second_postpool Strawberry -0.320 0.1412 10759
## second_postpool Cranberry - first_prepool Strawberry 1.098 0.1451 1033
## second_postpool Cranberry - pool Strawberry 0.520 0.1403 4684
## second_postpool Cranberry - second_postpool Strawberry 0.100 0.0935 2401
## first_prepool Strawberry - pool Strawberry -0.577 0.1382 2390
## first_prepool Strawberry - second_postpool Strawberry -0.997 0.1115 866
## pool Strawberry - second_postpool Strawberry -0.420 0.1052 29408
## t.ratio p.value
## -4.177 0.0010
## -8.942 <.0001
## 1.711 0.7402
## -2.502 0.2311
## -5.683 <.0001
## 2.712 0.1436
## -1.884 0.6247
## -4.943 <.0001
## -3.993 0.0021
## 4.360 0.0005
## 1.711 0.7402
## -1.782 0.6942
## 4.920 <.0001
## 2.712 0.1436
## -1.074 0.9779
## 7.935 <.0001
## 4.190 0.0009
## 1.711 0.7402
## 8.574 <.0001
## 4.883 <.0001
## 2.712 0.1436
## -4.177 0.0010
## -8.942 <.0001
## 1.074 0.9779
## -2.858 0.0995
## -6.144 <.0001
## -3.993 0.0021
## 4.061 0.0017
## 1.074 0.9779
## -2.264 0.3646
## 7.566 <.0001
## 3.710 0.0065
## 1.074 0.9779
## -4.177 0.0010
## -8.942 <.0001
## -3.993 0.0021
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 9 estimates2.3 Plot
#Position dodge
pd <- ggplot2::position_dodge(0.3) # move them .05 to the left and right
#Extract slope and intercept
dat_predict_allfruits <- expand.grid(Generation=as.numeric(levels(as.factor(data_sum$Generation))),
Fruit_s=unique(data_sum$Fruit_s))
dat_predict_allfruits$Phase <- ifelse(dat_predict_allfruits$Generation<= 7, "first_prepool",
ifelse(dat_predict_allfruits$Generation>=12, "second_postpool", "pool"))
dat_predict_allfruits$fitness_predicted <- predict(mod_Phase, newdata = dat_predict_allfruits,
re.form= NA, type = "response")
#REAL DATA
#Add G1 G6 and G7
TEMP_lineG1 <- c(rep(NA, 2), 1,rep(NA, 5))
TEMP_lineG6 <- c(rep(NA, 2), 6,rep(NA, 5))
TEMP_lineG7 <- c(rep(NA, 2), 7,rep(NA, 5))
TEMP_total <- rbind(data_sum,TEMP_lineG1,TEMP_lineG1,TEMP_lineG1,TEMP_lineG6,TEMP_lineG6,TEMP_lineG6,TEMP_lineG7,TEMP_lineG7,TEMP_lineG7)
TEMP_total$Fruit_s[TEMP_total$Generation == "6"] <- c("Strawberry", "Cranberry", "Cherry")
TEMP_total$Fruit_s[TEMP_total$Generation == "1"] <- c("Strawberry", "Cranberry", "Cherry")
TEMP_total$Fruit_s[TEMP_total$Generation == "7"] <- c("Strawberry", "Cranberry", "Cherry")
tail(TEMP_total)## Line Fruit_s Generation Phase N Nb_adults sd fitness se_fitness
## 251 <NA> Strawberry 6 <NA> NA NA NA NA NA
## 252 <NA> Cranberry 6 <NA> NA NA NA NA NA
## 253 <NA> Cherry 6 <NA> NA NA NA NA NA
## 254 <NA> Strawberry 7 <NA> NA NA NA NA NA
## 255 <NA> Cranberry 7 <NA> NA NA NA NA NA
## 256 <NA> Cherry 7 <NA> NA NA NA NA NA## Add label
TEMP_anno <- data.frame(x1 = c(3.5, 3.5, 10, 3.5, 3.5, 10, 3.5, 3.5, 10),
x2 = c(9, 17.5, 17.5,9, 17.5, 17.5,9,17.5, 17.5),
y1 = c(1,2,1.2,1,2,1.2,1,2,1.2),
y2 = c(1.25, 2.25, 1.45, 1.25, 2.25, 1.45,1.25, 2.25, 1.45),
xstar = c(6.5,10,14,6.5,10,14,6.5,10,14),
ystar = c(1.5,2.5,1.7,1.5,2.5,1.7,1.5,2.5,1.7),
lab = c("**", "***", "**", "**", "***", "**", "**" ,"***", "**"),
Fruit_s = c("Cherry", "Cherry", "Cherry",
"Cranberry", "Cranberry", "Cranberry",
"Strawberry","Strawberry","Strawberry"),
Line = NA)
TEMP_anno## x1 x2 y1 y2 xstar ystar lab Fruit_s Line
## 1 3.5 9.0 1.0 1.25 6.5 1.5 ** Cherry NA
## 2 3.5 17.5 2.0 2.25 10.0 2.5 *** Cherry NA
## 3 10.0 17.5 1.2 1.45 14.0 1.7 ** Cherry NA
## 4 3.5 9.0 1.0 1.25 6.5 1.5 ** Cranberry NA
## 5 3.5 17.5 2.0 2.25 10.0 2.5 *** Cranberry NA
## 6 10.0 17.5 1.2 1.45 14.0 1.7 ** Cranberry NA
## 7 3.5 9.0 1.0 1.25 6.5 1.5 ** Strawberry NA
## 8 3.5 17.5 2.0 2.25 10.0 2.5 *** Strawberry NA
## 9 10.0 17.5 1.2 1.45 14.0 1.7 ** Strawberry NATEMP_title <- data.frame(xtitle = c(14, 14, 14),
ytitle = c(3, 3, 3),
title = c("Cherry", "Cranberry", "Strawberry"),
Fruit_s = c("Cherry", "Cranberry", "Strawberry"),
Line = NA)
TEMP_title## xtitle ytitle title Fruit_s Line
## 1 14 3 Cherry Cherry NA
## 2 14 3 Cranberry Cranberry NA
## 3 14 3 Strawberry Strawberry NAPLOT_FITNESS_CHERRY <- ggplot(data = TEMP_total[TEMP_total$Fruit_s == "Cherry",],
aes(x = factor(Generation),group = Line, y = fitness, colour =Fruit_s)) +
geom_errorbar(aes(ymin =fitness-1.96*se_fitness, ymax = fitness + 1.96*se_fitness),
width=.1,position = pd, size = 0.2,color = "black") +
geom_line(size = 0.3,position = pd) +
geom_line(data = dat_predict_allfruits[dat_predict_allfruits$Fruit_s == "Cherry",],
aes(x = factor(Generation), y = fitness_predicted,
colour = "black", group = Phase), size = 0.5) +
geom_point(size =1, position = pd, shape =21, fill = "white") +
ylim(-3, 3.05) +
ylab("Fitness") +
xlab("Generation") +
geom_text(data = TEMP_anno[TEMP_anno$Fruit_s == "Cherry",], aes(x = xstar, y = ystar, label = lab), size =3.3) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Cherry",], aes(x = x1, xend = x1,
y = y1, yend = y2), size = 0.4) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Cherry",], aes(x = x2, xend = x2,
y = y1, yend = y2), size = 0.4) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Cherry",], aes(x = x1, xend = x2,
y = y2, yend = y2), size = 0.4) +
scale_color_manual(values = c("black", "#BC3C6D", "#FDB424", "#3FAA96")) +
ggtitle("Cherry") +
theme_LO_adaptation + theme(plot.title = element_text(color = "#BC3C6D"))
PLOT_FITNESS_CHERRY
PLOT_FITNESS_CRANB <- ggplot2::ggplot(data = TEMP_total[TEMP_total$Fruit_s == "Cranberry",],
aes(x = factor(Generation),group = Line, y = fitness, colour =Fruit_s)) +
geom_errorbar(aes(ymin =fitness-1.96*se_fitness, ymax = fitness + 1.96*se_fitness),
width=.1,position = pd, size = 0.2,color = "black") +
geom_line(size = 0.3,position = pd) +
geom_line(data = dat_predict_allfruits[dat_predict_allfruits$Fruit_s == "Cranberry",], aes(x = factor(Generation), y = fitness_predicted, colour = "black", group = Phase),
size = 0.5) +
geom_point(size =1, position = pd, shape =21, fill = "white") +
ylim(-3, 3.05) +
ylab("Fitness") +
xlab("Generation") +
geom_text(data = TEMP_anno[TEMP_anno$Fruit_s == "Cranberry",], aes(x = xstar, y = ystar, label = lab), size =3.3) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Cranberry",], aes(x = x1, xend = x1,
y = y1, yend = y2), size = 0.4) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Cranberry",], aes(x = x2, xend = x2,
y = y1, yend = y2), size = 0.4) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Cranberry",], aes(x = x1, xend = x2,
y = y2, yend = y2), size = 0.4) +
scale_color_manual(values = c("black", "#FDB424")) +
ggtitle("Cranberry") +
theme_LO_adaptation + theme(plot.title = element_text(color = "#FDB424"))
PLOT_FITNESS_CRANB
PLOT_FITNESS_STRAW <- ggplot2::ggplot(data = TEMP_total[TEMP_total$Fruit_s == "Strawberry",],
aes(x = factor(Generation),group = Line, y = fitness, colour =Fruit_s)) +
geom_errorbar(aes(ymin =fitness-1.96*se_fitness, ymax = fitness + 1.96*se_fitness),
width=.1,position = pd, size = 0.2,color = "black") +
geom_line(size = 0.3,position = pd) +
geom_line(data = dat_predict_allfruits[dat_predict_allfruits$Fruit_s == "Strawberry",],
aes(x = factor(Generation), y = fitness_predicted, colour = "black", group = Phase),
size = 0.5) +
geom_point(size =1, position = pd, shape =21, fill = "white") +
ylim(-3, 3.05) +
ylab("Fitness") +
xlab("Generation") +
geom_text(data = TEMP_anno[TEMP_anno$Fruit_s == "Strawberry",],
aes(x = xstar, y = ystar, label = lab), size =3.3) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Strawberry",],
aes(x = x1, xend = x1, y = y1, yend = y2), size = 0.4) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Strawberry",],
aes(x = x2, xend = x2, y = y1, yend = y2), size = 0.4) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Strawberry",],
aes(x = x1, xend = x2, y = y2, yend = y2), size = 0.4) +
scale_color_manual(values = c("black", "#3FAA96")) +
ggtitle("Strawberry") +
theme_LO_adaptation + theme(plot.title = element_text(color = "#3FAA96"))
PLOT_FITNESS_STRAW
DYNAMIQUE_THREE_JOIN <- cowplot::ggdraw() +
cowplot::draw_plot(PLOT_FITNESS_CHERRY + theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.line.x = element_blank()),
x = 0, y = 0.66, width = 1, height = 0.33) +
cowplot::draw_plot(PLOT_FITNESS_CRANB + theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.line.x = element_blank()),
x = 0, y = 0.33, width = 1, height = 0.33) +
cowplot::draw_plot(PLOT_FITNESS_STRAW,
x = 0, y = 00, width = 1, height = 0.33) +
cowplot::draw_plot_label(c("A", "B", "C"),
x = c(0, 0, 0),
y = c(1, 0.66, 0.33),
hjust = c(-0.5, -0.5, -0.5),
vjust = c(1.5, 1.5, 1.5),
size = 12)
DYNAMIQUE_THREE_JOIN
cowplot::save_plot(file =here::here("figures", "FIG_Adaptation.pdf"), DYNAMIQUE_THREE_JOIN, base_height = 17/cm(1), base_width = 11/cm(1), dpi = 1200)3 HETEROGENEITY
3.1 Plot fitness
pd <- position_dodge(width = 0.5)
################### INTERMEDIATE PHENOTYPING
# Re-order levels of Line
data_sum_G7 <- data_logchange[data_logchange$Generation == "7",]
data_sum_G7$Line <- factor(data_sum_G7$Line, levels= c("CE2", "CE1", "CE4", "CE3",
"CR2", "CR3", "CR5", "CR1", "CR4",
"FR2", "FR3", "FR5", "FR1", "FR4"))
#Plot:
symp_allop_g7 <- ggplot(data = data_sum_G7,
aes(x = Line, y = logchange, group = Treatment,
color = Fruit_s, shape = Treatment, fill = Fruit_s)) +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey", size = 0.5) +
geom_errorbar(aes(ymin = lowCIlogfitnesschange,
ymax = upCIlogfitnesschange),
width = 0.2, size = 0.5, alpha = 0.6,position = pd) +
geom_point(position = pd, fill = "white", size =3) +
ylab("Fitness change between intermediate\nand initial phenotyping steps") +
xlab("Populations") +
labs(shape = "Test fruit", color = "Selection fruit") +
guides(color = FALSE,
shape = guide_legend(override.aes = list(fill = c("black")))) +
scale_shape_manual(values = c(21, 22, 24)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96")) +
scale_fill_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96")) +
theme_LO_sober + theme(axis.text.x = element_blank()) +
coord_cartesian(expand = FALSE, ylim = c(-2.7, 2.7), xlim=c(0, 15),clip = "off") +
ggtitle("Intermediate phenotyping step") +
annotate("text", x = 2.5, y = 2.4, label = 'bold(" Evolved\non cherry")',
size =3,colour = "#BC3C6D",parse = TRUE) +
annotate("text", x = 7.5, y = 2.4, label = 'bold(" Evolved\non cranberry")',
size =3,colour = "#FDB424",parse = TRUE) +
annotate("text", x = 12.5, y = 2.4, label = 'bold(" Evolved\non strawberry")',
size =3,colour = "#3FAA96",parse = TRUE) +
geom_segment(x = 15.5, y = -0.1, xend= 15.5, yend = -1.6, size = 0.15,
arrow = arrow(length = unit(0.05, "npc")),colour = "black") +
geom_segment(x = 15.5, y = 0.1, xend= 15.5, yend = 1.6, size = 0.15,
arrow = arrow(length = unit(0.05, "npc")),colour = "black") +
annotate("text", x = 16.2, y = 0.7, label = 'bold(" Fitness\nincrease")',
size = 3,colour = "black", parse = TRUE, angle =90) +
annotate("text", x = 16.2, y =-0.7, label = 'bold(" Fitness\ndecrease")',
size = 3,colour = "black", parse = TRUE, angle =90)
symp_allop_g7
################### FINAL PHENOTYPING
# Re-order levels of Line
data_sum_G29 <- data_logchange[data_logchange$Generation == "29",]
data_sum_G29$Line <- factor(data_sum_G29$Line, levels = c("CEA", "CEB", "CEC",
"CRD", "CRA", "CRC", "CRB", "CRE",
"FRA", "FRC", "FRB"))
symp_allop_G29 <- ggplot(data = data_sum_G29,
aes(x = Line, y=logchange, group = Treatment,
color =Fruit_s, shape = Treatment,fill =Fruit_s)) +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey", size = 0.5) +
geom_point(size =3, position = pd) +
geom_errorbar(aes(ymin = lowCIlogfitnesschange,
ymax = upCIlogfitnesschange),
width= 0.2, size = 0.5, alpha = 0.6,position = pd) +
ylab("Fitness change between final\nand initial phenotyping steps") +
xlab("Populations") +
labs(shape = "Test fruit", color = "Selection fruit") +
guides(fill = FALSE, alpha = FALSE) +
scale_shape_manual(values = c(21, 22, 24)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96")) +
scale_fill_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96")) +
theme_LO_sober + theme(axis.text.x = element_blank()) +
coord_cartesian(expand = FALSE, ylim = c(-2, 2), xlim=c(0, 11),clip = "off") +
annotate("text", x = 2, y =1.5, label = 'bold(" Evolved\non cherry")',
size =3,colour = "#BC3C6D",parse = TRUE) +
annotate("text", x = 6, y =1.5, label = 'bold(" Evolved\non cranberry")',
size =3,colour = "#FDB424",parse = TRUE) +
annotate("text", x = 10, y =1.5, label = 'bold(" Evolved\non strawberry")',
size =3,colour = "#3FAA96",parse = TRUE) +
geom_segment(x = 11.5, y = -0.1, xend= 11.5, yend = -1.2, size = 0.15,
arrow = arrow(length = unit(0.05, "npc")),colour = "black") +
geom_segment(x = 11.5, y = 0.1, xend= 11.5, yend = 1.2, size = 0.15,
arrow = arrow(length = unit(0.05, "npc")),colour = "black") +
annotate("text", x = 12, y = 0.5, label = 'bold(" Fitness\nincrease")',
size =3,colour = "black",parse = TRUE, angle =90) +
annotate("text", x = 12, y = -0.5, label = 'bold(" Fitness\ndecrease")',
size =3,colour = "black", parse = TRUE, angle =90) +
ggtitle("Final phenotyping step")
symp_allop_G29
### Add stroke
DIF_FITNESS_G7 <- symp_allop_g7 +
geom_point(aes(alpha = SA, color = interaction(SA, Fruit_s)),
position = pd, size =3, stroke = 1.5, fill = "white") +
scale_alpha_manual(values = c(0, 1)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96",
"#7C2748", "#CA8702", "#328677",
"#BC3C6D", "#FDB424", "#3FAA96"))
DIF_FITNESS_G7
DIF_FITNESS_G29 <- symp_allop_G29 + geom_point(aes(alpha = SA, color = interaction(SA,Fruit_s)), position = pd, size =3, stroke =1.5) +
scale_alpha_manual(values = c(0, 1)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96", "#7C2748", "#CA8702", "#328677", "#BC3C6D", "#FDB424", "#3FAA96"))
DIF_FITNESS_G29
plot_legend <- ggplot(data = data_logchange, aes(x = Line, y=logchange, group = Treatment,
color =Fruit_s, shape = Treatment,fill =Fruit_s)) +
geom_point(size =3, position = pd) +
labs(shape = "Test fruit", color = "Selection fruit") +
scale_shape_manual(values = c(21, 22, 24)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96")) +
theme_LO_sober +
guides(fill = FALSE) +
theme(legend.key.size = unit(0.5, "cm"),
axis.text.x = element_blank()) +
coord_cartesian(expand = FALSE, ylim = c(-2, 2), xlim=c(0, 11),clip = "off")
###########################################################
#############################################ALL PLOT
legend_trade <- lemon::g_legend(plot_legend)
SYMP_ALLOP_TOTAL <- cowplot::ggdraw() +
cowplot::draw_plot(DIF_FITNESS_G7 + theme(legend.position = 'none',
plot.margin =unit(c(0.5, 3.5, 0.5, 0.5), "cm")),
x = 0, y = 0.5, width = 1, height = 0.5) +
cowplot::draw_plot(DIF_FITNESS_G29 + theme(legend.position = 'none',
plot.margin =unit(c(0.5, 3.5, 0.5, 0.5), "cm")),
x = 0, y = 0, width = 1, height = 0.5) +
cowplot::draw_plot_label(c("A", "B"),
x = c(0, 0), y = c(1, 0.5),
size = 14) +
cowplot::draw_plot(legend_trade, x = 0.93, y = 0.5, width = 0.001, height = 0.001)
SYMP_ALLOP_TOTAL
cowplot::save_plot(file =here::here("figures", "FIG_Heterogeneity.pdf"),
SYMP_ALLOP_TOTAL, base_height = 20/cm(1), base_width = 25/cm(1), dpi = 1200)3.2 Plot fecundity
pd <- position_dodge(width = 0.5)
################### INTERMEDIATE PHENOTYPING
symp_allop_g7_FECUNDITY <- ggplot(data = data_sum_G7,
aes(x = Line, y = logfecundchange, group = Treatment,
color = Fruit_s, shape = Treatment, fill = Fruit_s)) +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey", size = 0.5) +
geom_errorbar(aes(ymin = lowCIlogfecundchange,
ymax = upCIlogfecundchange, linetype = "dashed"),
width = 0.2, size = 0.5, alpha = 0.6, position = pd) +
geom_point(position = pd, fill = "white", size =3) +
ylab("Change in log fecundity between\nintermediate and initial phenotyping steps") +
xlab("Populations") +
labs(shape = "Test fruit", color = "Selection fruit") +
guides(color = FALSE,
shape = guide_legend(override.aes = list(fill = c("black")))) +
scale_shape_manual(values = c(21, 22, 24)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96")) +
scale_fill_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96")) +
theme_LO_sober + theme(axis.text.x = element_blank()) +
coord_cartesian(expand = FALSE, ylim = c(-1.1, 2), xlim=c(0, 15),clip = "off") +
ggtitle("Intermediate phenotyping step") +
annotate("text", x = 2.5, y = 1.8, label = 'bold(" Evolved\non cherry")',
size =3,colour = "#BC3C6D",parse = TRUE) +
annotate("text", x = 7.5, y = 1.8, label = 'bold(" Evolved\non cranberry")',
size =3,colour = "#FDB424",parse = TRUE) +
annotate("text", x = 12.5, y = 1.8, label = 'bold(" Evolved\non strawberry")',
size =3,colour = "#3FAA96",parse = TRUE)
symp_allop_g7_FECUNDITY
################### FINAL PHENOTYPING
symp_allop_G29_FECUNDITY <- ggplot(data = data_sum_G29,
aes(x = Line, y=logfecundchange, group = Treatment,
color =Fruit_s, shape = Treatment,fill =Fruit_s)) +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey", size = 0.5) +
geom_point(size =3, position = pd) +
geom_errorbar(aes(ymin =lowCIlogfecundchange, ymax =upCIlogfecundchange),
width= 0.2, size = 0.5, alpha = 0.6,position = pd) +
ylab("Change in log fecundity between\nfinal and initial phenotyping steps") +
xlab("Populations") +
labs(shape = "Test fruit", color = "Selection fruit") +
guides(fill = FALSE, alpha = FALSE) +
scale_shape_manual(values = c(21, 22, 24)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96")) +
scale_fill_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96")) +
theme_LO_sober + theme(axis.text.x = element_blank()) +
coord_cartesian(expand = FALSE, ylim = c(-1.1, 2), xlim=c(0, 11),clip = "off") +
annotate("text", x = 2, y = 1.4, label = 'bold(" Evolved\non cherry")',
size =3,colour = "#BC3C6D",parse = TRUE) +
annotate("text", x = 6, y = 1.4, label = 'bold(" Evolved\non cranberry")',
size =3,colour = "#FDB424",parse = TRUE) +
annotate("text", x = 10, y = 1.4, label = 'bold(" Evolved\non strawberry")',
size =3,colour = "#3FAA96",parse = TRUE) +
ggtitle("Final phenotyping step")
symp_allop_G29_FECUNDITY
### Add stroke
DIF_FECUNDITY_G7 <- symp_allop_g7_FECUNDITY +
geom_point(aes(alpha = SA, color = interaction(SA, Fruit_s)), position = pd, size =3, stroke =1.5, fill = "white") +
scale_alpha_manual(values = c(0, 1)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96", "#7C2748", "#CA8702", "#328677", "#BC3C6D", "#FDB424", "#3FAA96"))
DIF_FECUNDITY_G7
DIF_FECUNDITY_G29 <- symp_allop_G29_FECUNDITY +
geom_point(aes(alpha = SA, color = interaction(SA,Fruit_s)),
position = pd, size =3, stroke =1.5) +
scale_alpha_manual(values = c(0, 1)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96", "#7C2748", "#CA8702", "#328677", "#BC3C6D", "#FDB424", "#3FAA96"))
DIF_FECUNDITY_G29
plot_legend <- ggplot(data = data_logchange, aes(x = Line, y=logchange, group = Treatment,
color =Fruit_s, shape = Treatment,fill =Fruit_s)) +
geom_point(size =3, position = pd) +
labs(shape = "Test fruit", color = "Selection fruit") +
scale_shape_manual(values = c(21, 22, 24)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96")) +
theme_LO_sober +
guides(fill = FALSE) +
theme(legend.key.size = unit(0.5, "cm"),
axis.text.x = element_blank()) +
coord_cartesian(expand = FALSE, ylim = c(-2, 2), xlim=c(0, 11),clip = "off")
###########################################################
#############################################ALL PLOT
legend_trade <- lemon::g_legend(plot_legend)
SYMP_ALLOP_TOTAL_FEC <- cowplot::ggdraw() +
cowplot::draw_plot(DIF_FECUNDITY_G7 + theme(legend.position = 'none',
plot.margin =unit(c(0.5, 3.5, 0.5, 0.5), "cm")),
x = 0, y = 0.5, width = 1, height = 0.5) +
cowplot::draw_plot(DIF_FECUNDITY_G29 + theme(legend.position = 'none',
plot.margin =unit(c(0.5, 3.5, 0.5, 0.5), "cm")),
x = 0, y = 0, width = 1, height = 0.5) +
cowplot::draw_plot_label(c("A", "B"),
x = c(0, 0), y = c(1, 0.5),
size = 14) +
cowplot::draw_plot(legend_trade, x = 0.93, y = 0.5, width = 0.001, height = 0.001)
#
# cowplot::save_plot(file =here::here("figures", "FIG_SX_HeterogeneityNb_eggs.pdf"),
# SYMP_ALLOP_TOTAL_FEC, base_height = 20/cm(1), base_width = 25/cm(1), dpi = 1200)3.3 Plot egg-to-adult viability
pd <- position_dodge(width = 0.5)
################### INTERMEDIATE PHENOTYPING
symp_allop_g7_VIABILITIY <- ggplot(data = data_sum_G7,
aes(x = Line, y = logeggtoadchange, group = Treatment,
color = Fruit_s, shape = Treatment, fill = Fruit_s)) +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey", size = 0.5) +
geom_errorbar(aes(ymin = lowCIlogeggtoadchange,
ymax =upCIlogeggtoadchange, linetype = "dashed"),
width = 0.2, size = 0.5, alpha = 0.6,position = pd) +
geom_point(position = pd, fill = "white", size =3) +
ylab("Change in logit egg-to-adult viability\nbetween intermediate and initial phenotyping steps") +
xlab("Populations") +
labs(shape = "Test fruit", color = "Selection fruit") +
guides(color = FALSE,
shape = guide_legend(override.aes = list(fill = c("black")))) +
scale_shape_manual(values = c(21, 22, 24)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96")) +
scale_fill_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96")) +
theme_LO_sober + theme(axis.text.x = element_blank()) +
coord_cartesian(expand = FALSE, ylim = c(-1.7, 1.6), xlim=c(0, 15),clip = "off") +
ggtitle("Intermediate phenotyping step") +
annotate("text", x = 2.5, y = 1.4, label = 'bold(" Evolved\non cherry")',
size =3,colour = "#BC3C6D",parse = TRUE) +
annotate("text", x = 7.5, y = 1.4, label = 'bold(" Evolved\non cranberry")',
size =3,colour = "#FDB424",parse = TRUE) +
annotate("text", x = 12.5, y = 1.4, label = 'bold(" Evolved\non strawberry")',
size =3,colour = "#3FAA96",parse = TRUE)
symp_allop_g7_VIABILITIY
################### FINAL PHENOTYPING
symp_allop_G29_VIABILITIY <- ggplot(data = data_sum_G29,
aes(x = Line, y=logeggtoadchange, group = Treatment,
color =Fruit_s, shape = Treatment,fill =Fruit_s)) +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey", size = 0.5) +
geom_point(size =3, position = pd) +
geom_errorbar(aes(ymin =lowCIlogeggtoadchange, ymax =upCIlogeggtoadchange),
width= 0.2, size = 0.5, alpha = 0.6,position = pd) +
ylab("Change in logit egg-to-adult viability\nbetween final and initial phenotyping steps") +
xlab("Populations") +
labs(shape = "Test fruit", color = "Selection fruit") +
guides(fill = FALSE, alpha = FALSE) +
scale_shape_manual(values = c(21, 22, 24)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96")) +
scale_fill_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96")) +
theme_LO_sober + theme(axis.text.x = element_blank()) +
coord_cartesian(expand = FALSE, ylim = c(-1.7, 1.6), xlim=c(0, 11),clip = "off") +
annotate("text", x = 2, y = 1.4, label = 'bold(" Evolved\non cherry")',
size =3,colour = "#BC3C6D",parse = TRUE) +
annotate("text", x = 6, y = 1.4, label = 'bold(" Evolved\non cranberry")',
size =3,colour = "#FDB424",parse = TRUE) +
annotate("text", x = 10, y = 1.4, label = 'bold(" Evolved\non strawberry")',
size =3,colour = "#3FAA96",parse = TRUE) +
ggtitle("Final phenotyping step")
symp_allop_G29_VIABILITIY
### Add stroke
DIF_VIABILITY_G7 <- symp_allop_g7_VIABILITIY +
geom_point(aes(alpha = SA, color = interaction(SA,Fruit_s)), position = pd, size =3, stroke =1.5, fill = "white") +
scale_alpha_manual(values = c(0, 1)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96", "#7C2748", "#CA8702", "#328677", "#BC3C6D", "#FDB424", "#3FAA96"))
DIF_VIABILITY_G7
DIF_VIABILITY_G29 <- symp_allop_G29_VIABILITIY +
geom_point(aes(alpha = SA, color = interaction(SA,Fruit_s)),
position = pd, size =3, stroke =1.5) +
scale_alpha_manual(values = c(0, 1)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96", "#7C2748", "#CA8702", "#328677", "#BC3C6D", "#FDB424", "#3FAA96"))
DIF_VIABILITY_G29
plot_legend <- ggplot(data = data_logchange, aes(x = Line, y=logchange, group = Treatment,
color =Fruit_s, shape = Treatment,fill =Fruit_s)) +
geom_point(size =3, position = pd) +
labs(shape = "Test fruit", color = "Selection fruit") +
scale_shape_manual(values = c(21, 22, 24)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96")) +
theme_LO_sober +
guides(fill = FALSE) +
theme(legend.key.size = unit(0.5, "cm"),
axis.text.x = element_blank()) +
coord_cartesian(expand = FALSE, ylim = c(-2, 2), xlim=c(0, 11),clip = "off")
###########################################################
#############################################ALL PLOT
legend_trade <- lemon::g_legend(plot_legend)
SYMP_ALLOP_TOTAL_VIABILITY <- cowplot::ggdraw() +
cowplot::draw_plot(DIF_VIABILITY_G7 + theme(legend.position = 'none',
plot.margin =unit(c(0.5, 3.5, 0.5, 0.5), "cm")),
x = 0, y = 0.5, width = 1, height = 0.5) +
cowplot::draw_plot(DIF_VIABILITY_G29 + theme(legend.position = 'none',
plot.margin =unit(c(0.5, 3.5, 0.5, 0.5), "cm")),
x = 0, y = 0, width = 1, height = 0.5) +
cowplot::draw_plot_label(c("A", "B"),
x = c(0, 0), y = c(1, 0.5),
size = 14) +
cowplot::draw_plot(legend_trade, x = 0.93, y = 0.5, width = 0.001, height = 0.001)
SYMP_ALLOP_TOTAL_VIABILITY
# cowplot::save_plot(file =here::here("figures", "FIG_SX_HeterogeneityEgg-to-adultviability.pdf"),
# SYMP_ALLOP_TOTAL_VIABILITY, base_height = 20/cm(1), base_width = 25/cm(1), dpi = 1200)
#################### ALL SUPPLEMENTS: Fecundity and Viability
DIF_SYMP_ALLOP_TOTAL_SUPPLEMENTS <- cowplot::ggdraw() +
cowplot::draw_plot(DIF_FECUNDITY_G7 + theme(legend.position = 'none',
plot.margin =unit(c(0.5, 3.5, 0.5, 0.5), "cm"),
axis.title.x=element_text(size = 12),
axis.title.y=element_text(size = 12)),
x = 0, y = 0.45, width = 0.55, height = 0.5) +
cowplot::draw_plot(DIF_FECUNDITY_G29 + theme(legend.position = 'none',
plot.margin =unit(c(0.5, 3.5, 0.5, 0.5), "cm"),
axis.title.x=element_text(size = 12),
axis.title.y=element_text(size = 12)),
x = 0, y = 0, width = 0.55, height = 0.5) +
cowplot::draw_plot(DIF_VIABILITY_G7 + theme(legend.position = 'none',
plot.margin =unit(c(0.5, 3.5, 0.5, 0.5),"cm"),
axis.title.x=element_text(size = 12),
axis.title.y=element_text(size = 12)),
x = 0.45, y = 0.45, width = 0.55, height = 0.5) +
cowplot::draw_plot(DIF_VIABILITY_G29 + theme(legend.position = 'none',
plot.margin =unit(c(0.5, 3.5, 0.5, 0.5), "cm"),
axis.title.x=element_text(size = 12),
axis.title.y=element_text(size = 12)),
x = 0.45, y = 0, width = 0.55, height = 0.5) +
cowplot::draw_plot_label(c("A", "Fecundity", "B", "Egg-to-adult viability"),
x = c(0, 0.15, 0.45, 0.53), y = c(1, 1, 1, 1),
size = c(17, 15, 17, 15)) +
cowplot::draw_plot(legend_trade, x = 0.94, y = 0.5, width = 0.001, height = 0.001)
DIF_SYMP_ALLOP_TOTAL_SUPPLEMENTS
cowplot::save_plot(file =here::here("figures", "FIG_SX_Heterogeneity_Fecundity_Viability.pdf"),
DIF_SYMP_ALLOP_TOTAL_SUPPLEMENTS,
base_height = 20/cm(1), base_width = 30/cm(1), dpi = 1200)3.4 Analysis fitness
3.4.1 Compare fit of Poisson lognormal and negative binomial distributions
## Poisson lognormal model
mpoislognormal <- lme4::glmer(Nb_adults ~ Generation*Fruit_s*Treatment + (1|Obs), family="poisson", data=data)
summary(mpoislognormal)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: Nb_adults ~ Generation * Fruit_s * Treatment + (1 | Obs)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 14644.6 14763.9 -7300.3 14600.6 1652
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.98315 -0.16385 0.04689 0.14592 0.34552
##
## Random effects:
## Groups Name Variance Std.Dev.
## Obs (Intercept) 0.5636 0.7507
## Number of obs: 1674, groups: Obs, 1674
##
## Fixed effects:
## Estimate Std. Error z value
## (Intercept) 3.04363 0.07866 38.693
## Generation29 0.17011 0.11412 1.491
## Generation7 -0.08779 0.16053 -0.547
## Fruit_sCranberry -0.04282 0.19499 -0.220
## Fruit_sStrawberry 0.58170 0.17148 3.392
## TreatmentCranberry 0.07202 0.11097 0.649
## TreatmentStrawberry 0.16668 0.11082 1.504
## Generation29:Fruit_sCranberry 0.06431 0.22120 0.291
## Generation29:Fruit_sStrawberry -0.77126 0.20775 -3.712
## Generation29:TreatmentCranberry -0.55720 0.16196 -3.440
## Generation7:TreatmentCranberry -0.15416 0.23335 -0.661
## Generation29:TreatmentStrawberry -0.22375 0.16108 -1.389
## Generation7:TreatmentStrawberry 0.29309 0.23243 1.261
## Fruit_sCranberry:TreatmentCranberry 0.36292 0.27293 1.330
## Fruit_sStrawberry:TreatmentCranberry -0.12228 0.24988 -0.489
## Fruit_sCranberry:TreatmentStrawberry 0.04192 0.27801 0.151
## Fruit_sStrawberry:TreatmentStrawberry -0.41137 0.24640 -1.669
## Generation29:Fruit_sCranberry:TreatmentCranberry 0.05299 0.31080 0.171
## Generation29:Fruit_sStrawberry:TreatmentCranberry 0.64959 0.30038 2.163
## Generation29:Fruit_sCranberry:TreatmentStrawberry -0.56909 0.31524 -1.805
## Generation29:Fruit_sStrawberry:TreatmentStrawberry 0.91491 0.29665 3.084
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## Generation29 0.136051
## Generation7 0.584439
## Fruit_sCranberry 0.826176
## Fruit_sStrawberry 0.000693 ***
## TreatmentCranberry 0.516306
## TreatmentStrawberry 0.132574
## Generation29:Fruit_sCranberry 0.771276
## Generation29:Fruit_sStrawberry 0.000205 ***
## Generation29:TreatmentCranberry 0.000581 ***
## Generation7:TreatmentCranberry 0.508838
## Generation29:TreatmentStrawberry 0.164825
## Generation7:TreatmentStrawberry 0.207314
## Fruit_sCranberry:TreatmentCranberry 0.183617
## Fruit_sStrawberry:TreatmentCranberry 0.624596
## Fruit_sCranberry:TreatmentStrawberry 0.880156
## Fruit_sStrawberry:TreatmentStrawberry 0.095022 .
## Generation29:Fruit_sCranberry:TreatmentCranberry 0.864610
## Generation29:Fruit_sStrawberry:TreatmentCranberry 0.030575 *
## Generation29:Fruit_sCranberry:TreatmentStrawberry 0.071035 .
## Generation29:Fruit_sStrawberry:TreatmentStrawberry 0.002041 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 15 columns / coefficients
## convergence code: 0
## Model failed to converge with max|grad| = 0.0517486 (tol = 0.002, component 1)## Check convergence
derivs1 <- mpoislognormal@optinfo$derivs
sc_grad1 <- with(derivs1,solve(Hessian,gradient))
max(abs(sc_grad1))## [1] 0.005763628## Negative binomial model
mnegbin <- MASS::glm.nb(Nb_adults ~ Generation*Fruit_s*Treatment, data=data)
## Compare AIC
AIC(mnegbin, mpoislognormal)## df AIC
## mnegbin 22 14360.58
## mpoislognormal 22 14644.57## Simulate data with Poisson lognormal distribution
x.teo.poislognormal <- unlist(simulate(mpoislognormal))
x.teo.negbin <- unlist(simulate(mnegbin))
## QQplot to compared Negative binomial and Poisson log normal distributions
qqplot(data$Nb_adults, x.teo.negbin, main="QQ-plot", xlab="Observed data", ylab="Simulated data", las=1, xlim = c(0, 100), ylim = c(0, 100)) ## QQ-plot
points(sort(data$Nb_adults), sort(x.teo.poislognormal), pch=1, col='red')
abline(0,1)
## Add legend
legend(0, 70, legend=c("Neg. Bin.", "Pois. Lognormal"), col=c("black", "red"), pch=1, bty="n")
## Negative binomial distribution provides a better fit to the data3.4.2 Compare average fitess changes
m0 <- MASS::glm.nb(Nb_adults ~ Generation*Fruit_s, data=data)
summary(m0)##
## Call:
## MASS::glm.nb(formula = Nb_adults ~ Generation * Fruit_s, data = data,
## init.theta = 1.968231636, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.4615 -0.7782 -0.0830 0.4461 2.8089
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.2805352 0.0426489 76.920 < 2e-16 ***
## Generation29 0.0136542 0.0619522 0.220 0.8256
## Generation7 0.0007216 0.0899488 0.008 0.9936
## Fruit_sCranberry 0.0951520 0.1052162 0.904 0.3658
## Fruit_sGF NA NA NA NA
## Fruit_sStrawberry 0.3909076 0.0957667 4.082 4.47e-05 ***
## Generation29:Fruit_sCranberry -0.0627878 0.1195761 -0.525 0.5995
## Generation7:Fruit_sCranberry NA NA NA NA
## Generation29:Fruit_sGF NA NA NA NA
## Generation7:Fruit_sGF NA NA NA NA
## Generation29:Fruit_sStrawberry -0.1903868 0.1148241 -1.658 0.0973 .
## Generation7:Fruit_sStrawberry NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(1.9682) family taken to be 1)
##
## Null deviance: 1941.9 on 1673 degrees of freedom
## Residual deviance: 1890.4 on 1667 degrees of freedom
## AIC: 14428
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 1.9682
## Std. Err.: 0.0724
##
## 2 x log-likelihood: -14412.2880## G7: fitness increase for strawberry populations, fitness increased for cherry and cranberry populations
## G29: fitness increase for strawberry populations, no fitness change for cherry and cranberry populations
m1 <- MASS::glm.nb(Nb_adults ~ -1 + Generation_Fruit_s_Treatment, data=data)
summary(m1)##
## Call:
## MASS::glm.nb(formula = Nb_adults ~ -1 + Generation_Fruit_s_Treatment,
## data = data, init.theta = 2.092227879, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.5666 -0.7954 -0.0810 0.4562 3.7626
##
## Coefficients:
## Estimate Std. Error
## Generation_Fruit_s_Treatment0_GF_Cherry 3.21848 0.07197
## Generation_Fruit_s_Treatment0_GF_Cranberry 3.26385 0.07185
## Generation_Fruit_s_Treatment0_GF_Strawberry 3.35446 0.07162
## Generation_Fruit_s_Treatment29_Cherry_Cherry 3.47438 0.07520
## Generation_Fruit_s_Treatment29_Cherry_Cranberry 2.94034 0.07680
## Generation_Fruit_s_Treatment29_Cherry_Strawberry 3.39002 0.07540
## Generation_Fruit_s_Treatment29_Cranberry_Cherry 3.44744 0.05830
## Generation_Fruit_s_Treatment29_Cranberry_Cranberry 3.47176 0.05825
## Generation_Fruit_s_Treatment29_Cranberry_Strawberry 2.99139 0.05934
## Generation_Fruit_s_Treatment29_Strawberry_Cherry 3.28175 0.07568
## Generation_Fruit_s_Treatment29_Strawberry_Cranberry 3.41955 0.07533
## Generation_Fruit_s_Treatment29_Strawberry_Strawberry 3.72917 0.07468
## Generation_Fruit_s_Treatment7_Cherry_Cherry 3.19356 0.12735
## Generation_Fruit_s_Treatment7_Cherry_Cranberry 3.12613 0.13652
## Generation_Fruit_s_Treatment7_Cherry_Strawberry 3.50322 0.13718
## Generation_Fruit_s_Treatment7_Cranberry_Cherry 3.17683 0.12363
## Generation_Fruit_s_Treatment7_Cranberry_Cranberry 3.38900 0.10784
## Generation_Fruit_s_Treatment7_Cranberry_Strawberry 3.52468 0.12041
## Generation_Fruit_s_Treatment7_Strawberry_Cherry 3.71796 0.09074
## Generation_Fruit_s_Treatment7_Strawberry_Cranberry 3.56222 0.09343
## Generation_Fruit_s_Treatment7_Strawberry_Strawberry 3.71993 0.08790
## z value Pr(>|z|)
## Generation_Fruit_s_Treatment0_GF_Cherry 44.72 <2e-16 ***
## Generation_Fruit_s_Treatment0_GF_Cranberry 45.43 <2e-16 ***
## Generation_Fruit_s_Treatment0_GF_Strawberry 46.84 <2e-16 ***
## Generation_Fruit_s_Treatment29_Cherry_Cherry 46.20 <2e-16 ***
## Generation_Fruit_s_Treatment29_Cherry_Cranberry 38.29 <2e-16 ***
## Generation_Fruit_s_Treatment29_Cherry_Strawberry 44.96 <2e-16 ***
## Generation_Fruit_s_Treatment29_Cranberry_Cherry 59.14 <2e-16 ***
## Generation_Fruit_s_Treatment29_Cranberry_Cranberry 59.60 <2e-16 ***
## Generation_Fruit_s_Treatment29_Cranberry_Strawberry 50.41 <2e-16 ***
## Generation_Fruit_s_Treatment29_Strawberry_Cherry 43.36 <2e-16 ***
## Generation_Fruit_s_Treatment29_Strawberry_Cranberry 45.40 <2e-16 ***
## Generation_Fruit_s_Treatment29_Strawberry_Strawberry 49.93 <2e-16 ***
## Generation_Fruit_s_Treatment7_Cherry_Cherry 25.08 <2e-16 ***
## Generation_Fruit_s_Treatment7_Cherry_Cranberry 22.90 <2e-16 ***
## Generation_Fruit_s_Treatment7_Cherry_Strawberry 25.54 <2e-16 ***
## Generation_Fruit_s_Treatment7_Cranberry_Cherry 25.70 <2e-16 ***
## Generation_Fruit_s_Treatment7_Cranberry_Cranberry 31.43 <2e-16 ***
## Generation_Fruit_s_Treatment7_Cranberry_Strawberry 29.27 <2e-16 ***
## Generation_Fruit_s_Treatment7_Strawberry_Cherry 40.97 <2e-16 ***
## Generation_Fruit_s_Treatment7_Strawberry_Cranberry 38.13 <2e-16 ***
## Generation_Fruit_s_Treatment7_Strawberry_Strawberry 42.32 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(2.0922) family taken to be 1)
##
## Null deviance: 90823 on 1674 degrees of freedom
## Residual deviance: 1892 on 1653 degrees of freedom
## AIC: 14361
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 2.0922
## Std. Err.: 0.0780
##
## 2 x log-likelihood: -14316.5790## G0: no difference among different fruit media
## G7: fitness increase for strawberry populations on strawberry, but not on cherry or cranberry fruit media
## G7: no fitness change for cherry or cranberry populations
## G29: fitness increase for cherry populations on cherry medium, but not on strawberry or cranberry medium
## G29: fitness increase for strawberry populations on strawberry medium, but no change on cranberry medium and fitness decrease on cherry medium
## G29: fitness increase for cranberry populations on cranberry and cherry media, and fitness decrease on strawberry medium
library(lme4)
m2 <- lme4::glmer.nb(Nb_adults ~ -1 + Generation_Fruit_s_Treatment + (1|Line), data = data, verbose=TRUE)## th := est_theta(glmer(..)) = 2.201538 --> dev.= -2*logLik(.) = 14290.06
## 1: th= 1.084318957, dev=14594.96208819, beta[1]= 3.21950449
## 2: th= 4.469872861, dev=14690.23715275, beta[1]= 3.21930319
## 3: th= 0.4518359525, dev=15686.75531922, beta[1]= 3.21972111
## 4: th= 2.076154386, dev=14291.25743655, beta[1]= 3.22959061
## 5: th= 2.097889997, dev=14290.67362916, beta[1]= 3.21837130
## 6: th= 2.182950839, dev=14289.93574613, beta[1]= 3.21843046
## 7: th= 2.870326763, dev=14346.85616883, beta[1]= 3.21836162
## 8: th= 2.166741409, dev=14289.90664279, beta[1]= 3.21857142
## 9: th= 2.168737290, dev=14289.90604266, beta[1]= 3.21841254
## 10: th= 2.168773461, dev=14289.90604248, beta[1]= 3.21841248
## 11: th= 2.168809633, dev=14289.90604274, beta[1]= 3.21841325
## 12: th= 2.168773461, dev=14289.90604248, beta[1]= 3.21840813summary(m2)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Negative Binomial(2.1688) ( log )
## Formula: Nb_adults ~ -1 + Generation_Fruit_s_Treatment + (1 | Line)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 14335.9 14460.6 -7145.0 14289.9 1651
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4393 -0.6310 -0.0589 0.5331 8.2849
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line (Intercept) 0.02049 0.1431
## Number of obs: 1674, groups: Line, 26
##
## Fixed effects:
## Estimate Std. Error
## Generation_Fruit_s_Treatment0_GF_Cherry 3.21841 0.15968
## Generation_Fruit_s_Treatment0_GF_Cranberry 3.26379 0.15962
## Generation_Fruit_s_Treatment0_GF_Strawberry 3.35440 0.15952
## Generation_Fruit_s_Treatment29_Cherry_Cherry 3.47413 0.11094
## Generation_Fruit_s_Treatment29_Cherry_Cranberry 2.93116 0.11206
## Generation_Fruit_s_Treatment29_Cherry_Strawberry 3.38072 0.11106
## Generation_Fruit_s_Treatment29_Cranberry_Cherry 3.46660 0.08637
## Generation_Fruit_s_Treatment29_Cranberry_Cranberry 3.44527 0.08626
## Generation_Fruit_s_Treatment29_Cranberry_Strawberry 2.97878 0.08685
## Generation_Fruit_s_Treatment29_Strawberry_Cherry 3.23660 0.11148
## Generation_Fruit_s_Treatment29_Strawberry_Cranberry 3.38206 0.11123
## Generation_Fruit_s_Treatment29_Strawberry_Strawberry 3.72384 0.11068
## Generation_Fruit_s_Treatment7_Cherry_Cherry 3.19445 0.14794
## Generation_Fruit_s_Treatment7_Cherry_Cranberry 3.09115 0.15478
## Generation_Fruit_s_Treatment7_Cherry_Strawberry 3.50488 0.15520
## Generation_Fruit_s_Treatment7_Cranberry_Cherry 3.20505 0.13989
## Generation_Fruit_s_Treatment7_Cranberry_Cranberry 3.44650 0.12843
## Generation_Fruit_s_Treatment7_Cranberry_Strawberry 3.60078 0.14213
## Generation_Fruit_s_Treatment7_Strawberry_Cherry 3.67840 0.11704
## Generation_Fruit_s_Treatment7_Strawberry_Cranberry 3.53198 0.12019
## Generation_Fruit_s_Treatment7_Strawberry_Strawberry 3.68609 0.11504
## z value Pr(>|z|)
## Generation_Fruit_s_Treatment0_GF_Cherry 20.16 <2e-16 ***
## Generation_Fruit_s_Treatment0_GF_Cranberry 20.45 <2e-16 ***
## Generation_Fruit_s_Treatment0_GF_Strawberry 21.03 <2e-16 ***
## Generation_Fruit_s_Treatment29_Cherry_Cherry 31.32 <2e-16 ***
## Generation_Fruit_s_Treatment29_Cherry_Cranberry 26.16 <2e-16 ***
## Generation_Fruit_s_Treatment29_Cherry_Strawberry 30.44 <2e-16 ***
## Generation_Fruit_s_Treatment29_Cranberry_Cherry 40.14 <2e-16 ***
## Generation_Fruit_s_Treatment29_Cranberry_Cranberry 39.94 <2e-16 ***
## Generation_Fruit_s_Treatment29_Cranberry_Strawberry 34.30 <2e-16 ***
## Generation_Fruit_s_Treatment29_Strawberry_Cherry 29.03 <2e-16 ***
## Generation_Fruit_s_Treatment29_Strawberry_Cranberry 30.41 <2e-16 ***
## Generation_Fruit_s_Treatment29_Strawberry_Strawberry 33.64 <2e-16 ***
## Generation_Fruit_s_Treatment7_Cherry_Cherry 21.59 <2e-16 ***
## Generation_Fruit_s_Treatment7_Cherry_Cranberry 19.97 <2e-16 ***
## Generation_Fruit_s_Treatment7_Cherry_Strawberry 22.58 <2e-16 ***
## Generation_Fruit_s_Treatment7_Cranberry_Cherry 22.91 <2e-16 ***
## Generation_Fruit_s_Treatment7_Cranberry_Cranberry 26.84 <2e-16 ***
## Generation_Fruit_s_Treatment7_Cranberry_Strawberry 25.34 <2e-16 ***
## Generation_Fruit_s_Treatment7_Strawberry_Cherry 31.43 <2e-16 ***
## Generation_Fruit_s_Treatment7_Strawberry_Cranberry 29.39 <2e-16 ***
## Generation_Fruit_s_Treatment7_Strawberry_Strawberry 32.04 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1MuMIn::model.sel(m0, m1, m2)## Model selection table
## (Int) Frt_s Gnr Frt_s:Gnr Gnr_Frt_s_Trt family
## m2 + Negative Binomial(2.1688,log)
## m1 + Negative Binomial(2.0922,log)
## m0 3.281 + + + Negative Binomial(1.9682,log)
## class init.theta link random df logLik AICc delta weight
## m2 glmerMod L 23 -7144.953 14336.6 0.00 1
## m1 negbin 2.09 log 22 -7158.289 14361.2 24.62 0
## m0 negbin 1.97 log 8 -7206.144 14428.4 91.80 0
## Models ranked by AICc(x)
## Random terms:
## L = '1 | Line'## Get overdispersion parameter
getME(m2, "glmer.nb.theta")## [1] 2.168773## Check residuals for the different experimental populations
plot(m2, resid(.) ~ as.numeric(Line))
## Check random effect
# sjPlot::plot_model(m2, type="re", dot.size=1.5,
# group.terms = c(1, rep(2, nlevels(data_G7$Line)),
# rep(3, nlevels(data_G29$Line))))
mySumm <- function(.) {
c(beta=c(fixef(.)[1:3], fixef(.)[13:21] - rep(fixef(.)[1:3], 3), fixef(.)[4:12] - rep(fixef(.)[1:3], 3)), sigma=sqrt(unlist(VarCorr(.))))
}
boo01 <- lme4::bootMer(m2, mySumm, nsim = 500, re.form = NA)
head(data.frame(boo01))## beta.Generation_Fruit_s_Treatment0_GF_Cherry
## 1 3.389688
## 2 3.376950
## 3 3.388896
## 4 3.286045
## 5 3.196028
## 6 3.072877
## beta.Generation_Fruit_s_Treatment0_GF_Cranberry
## 1 3.393543
## 2 3.395640
## 3 3.371223
## 4 3.390066
## 5 3.181559
## 6 3.095099
## beta.Generation_Fruit_s_Treatment0_GF_Strawberry
## 1 3.476437
## 2 3.531176
## 3 3.526393
## 4 3.472006
## 5 3.303237
## 6 3.181598
## beta.Generation_Fruit_s_Treatment7_Cherry_Cherry
## 1 -0.15131529
## 2 -0.29875885
## 3 -0.27911892
## 4 -0.04208316
## 5 0.04068846
## 6 0.11389407
## beta.Generation_Fruit_s_Treatment7_Cherry_Cranberry
## 1 -0.2421151
## 2 -0.2980911
## 3 -0.5195561
## 4 0.1152746
## 5 -0.2326500
## 6 0.1356246
## beta.Generation_Fruit_s_Treatment7_Cherry_Strawberry
## 1 -0.007563546
## 2 -0.078876450
## 3 -0.051932924
## 4 0.207733832
## 5 0.192053488
## 6 0.283420285
## beta.Generation_Fruit_s_Treatment7_Cranberry_Cherry
## 1 -0.270361150
## 2 -0.074064317
## 3 -0.250297033
## 4 0.039107464
## 5 -0.002330064
## 6 0.117256084
## beta.Generation_Fruit_s_Treatment7_Cranberry_Cranberry
## 1 0.03029992
## 2 0.16309213
## 3 -0.17846913
## 4 0.20206953
## 5 0.08567410
## 6 0.40179789
## beta.Generation_Fruit_s_Treatment7_Cranberry_Strawberry
## 1 -0.06624386
## 2 0.14028317
## 3 0.02187438
## 4 0.12342269
## 5 0.26190644
## 6 0.32417088
## beta.Generation_Fruit_s_Treatment7_Strawberry_Cherry
## 1 0.3576928
## 2 0.2921236
## 3 0.4709825
## 4 0.2733298
## 5 0.5589026
## 6 0.8325958
## beta.Generation_Fruit_s_Treatment7_Strawberry_Cranberry
## 1 0.006228872
## 2 -0.079477457
## 3 0.212355194
## 4 -0.019436387
## 5 0.436972618
## 6 0.609439874
## beta.Generation_Fruit_s_Treatment7_Strawberry_Strawberry
## 1 0.1209654
## 2 0.1473214
## 3 0.3505781
## 4 -0.1061980
## 5 0.5711472
## 6 0.5206647
## beta.Generation_Fruit_s_Treatment29_Cherry_Cherry
## 1 0.18354186
## 2 0.12744675
## 3 -0.09596111
## 4 0.17003334
## 5 0.22634791
## 6 0.55017236
## beta.Generation_Fruit_s_Treatment29_Cherry_Cranberry
## 1 -0.48152101
## 2 -0.46714477
## 3 -0.56908366
## 4 -0.32994792
## 5 -0.22693093
## 6 -0.00993869
## beta.Generation_Fruit_s_Treatment29_Cherry_Strawberry
## 1 0.006403419
## 2 -0.060610653
## 3 -0.177856250
## 4 0.059111269
## 5 0.110419297
## 6 0.351542187
## beta.Generation_Fruit_s_Treatment29_Cranberry_Cherry
## 1 0.0004601943
## 2 0.1156391772
## 3 0.1054431315
## 4 0.2370840409
## 5 0.2347706641
## 6 0.4330759518
## beta.Generation_Fruit_s_Treatment29_Cranberry_Cranberry
## 1 0.03565671
## 2 -0.03133405
## 3 0.13905999
## 4 0.01422908
## 5 0.30556058
## 6 0.25281926
## beta.Generation_Fruit_s_Treatment29_Cranberry_Strawberry
## 1 -0.5114095
## 2 -0.6552197
## 3 -0.4789206
## 4 -0.5481690
## 5 -0.3782189
## 6 -0.0847164
## beta.Generation_Fruit_s_Treatment29_Strawberry_Cherry
## 1 -0.14699944
## 2 -0.24249834
## 3 -0.16481348
## 4 -0.17347761
## 5 0.19715214
## 6 0.04479471
## beta.Generation_Fruit_s_Treatment29_Strawberry_Cranberry
## 1 -0.06805847
## 2 -0.08591012
## 3 0.09135879
## 4 -0.11043459
## 5 0.22683881
## 6 0.19803686
## beta.Generation_Fruit_s_Treatment29_Strawberry_Strawberry sigma.Line
## 1 0.34945710 0.09837693
## 2 0.08106770 0.08077395
## 3 0.09462827 0.14427560
## 4 0.28616943 0.10234151
## 5 0.53310257 0.14807569
## 6 0.29639931 0.14933413## Extract all CIs
bCI_tab(boo01)## Estimate
## beta.Generation_Fruit_s_Treatment0_GF_Cherry 3.21840813
## beta.Generation_Fruit_s_Treatment0_GF_Cranberry 3.26378671
## beta.Generation_Fruit_s_Treatment0_GF_Strawberry 3.35439907
## beta.Generation_Fruit_s_Treatment7_Cherry_Cherry -0.02396108
## beta.Generation_Fruit_s_Treatment7_Cherry_Cranberry -0.17263265
## beta.Generation_Fruit_s_Treatment7_Cherry_Strawberry 0.15048578
## beta.Generation_Fruit_s_Treatment7_Cranberry_Cherry -0.01335955
## beta.Generation_Fruit_s_Treatment7_Cranberry_Cranberry 0.18271763
## beta.Generation_Fruit_s_Treatment7_Cranberry_Strawberry 0.24637782
## beta.Generation_Fruit_s_Treatment7_Strawberry_Cherry 0.45999087
## beta.Generation_Fruit_s_Treatment7_Strawberry_Cranberry 0.26818924
## beta.Generation_Fruit_s_Treatment7_Strawberry_Strawberry 0.33168853
## beta.Generation_Fruit_s_Treatment29_Cherry_Cherry 0.25572536
## beta.Generation_Fruit_s_Treatment29_Cherry_Cranberry -0.33262760
## beta.Generation_Fruit_s_Treatment29_Cherry_Strawberry 0.02632313
## beta.Generation_Fruit_s_Treatment29_Cranberry_Cherry 0.24819431
## beta.Generation_Fruit_s_Treatment29_Cranberry_Cranberry 0.18147832
## beta.Generation_Fruit_s_Treatment29_Cranberry_Strawberry -0.37561420
## beta.Generation_Fruit_s_Treatment29_Strawberry_Cherry 0.01818883
## beta.Generation_Fruit_s_Treatment29_Strawberry_Cranberry 0.11827014
## beta.Generation_Fruit_s_Treatment29_Strawberry_Strawberry 0.36944053
## sigma.Line 0.14312861
## X2.5..
## beta.Generation_Fruit_s_Treatment0_GF_Cherry 2.942776469
## beta.Generation_Fruit_s_Treatment0_GF_Cranberry 2.996934894
## beta.Generation_Fruit_s_Treatment0_GF_Strawberry 3.096732793
## beta.Generation_Fruit_s_Treatment7_Cherry_Cherry -0.460836284
## beta.Generation_Fruit_s_Treatment7_Cherry_Cranberry -0.603091163
## beta.Generation_Fruit_s_Treatment7_Cherry_Strawberry -0.299635582
## beta.Generation_Fruit_s_Treatment7_Cranberry_Cherry -0.445294794
## beta.Generation_Fruit_s_Treatment7_Cranberry_Cranberry -0.209674359
## beta.Generation_Fruit_s_Treatment7_Cranberry_Strawberry -0.158793709
## beta.Generation_Fruit_s_Treatment7_Strawberry_Cherry 0.068696585
## beta.Generation_Fruit_s_Treatment7_Strawberry_Cranberry -0.142499675
## beta.Generation_Fruit_s_Treatment7_Strawberry_Strawberry -0.055984513
## beta.Generation_Fruit_s_Treatment29_Cherry_Cherry -0.158702712
## beta.Generation_Fruit_s_Treatment29_Cherry_Cranberry -0.700439850
## beta.Generation_Fruit_s_Treatment29_Cherry_Strawberry -0.333814762
## beta.Generation_Fruit_s_Treatment29_Cranberry_Cherry -0.089445887
## beta.Generation_Fruit_s_Treatment29_Cranberry_Cranberry -0.139025997
## beta.Generation_Fruit_s_Treatment29_Cranberry_Strawberry -0.701155686
## beta.Generation_Fruit_s_Treatment29_Strawberry_Cherry -0.360277110
## beta.Generation_Fruit_s_Treatment29_Strawberry_Cranberry -0.270641171
## beta.Generation_Fruit_s_Treatment29_Strawberry_Strawberry 0.002465931
## sigma.Line 0.010502500
## X97.5..
## beta.Generation_Fruit_s_Treatment0_GF_Cherry 3.49903888
## beta.Generation_Fruit_s_Treatment0_GF_Cranberry 3.52494224
## beta.Generation_Fruit_s_Treatment0_GF_Strawberry 3.61072457
## beta.Generation_Fruit_s_Treatment7_Cherry_Cherry 0.39927346
## beta.Generation_Fruit_s_Treatment7_Cherry_Cranberry 0.27071914
## beta.Generation_Fruit_s_Treatment7_Cherry_Strawberry 0.55176419
## beta.Generation_Fruit_s_Treatment7_Cranberry_Cherry 0.38081092
## beta.Generation_Fruit_s_Treatment7_Cranberry_Cranberry 0.55986335
## beta.Generation_Fruit_s_Treatment7_Cranberry_Strawberry 0.63189757
## beta.Generation_Fruit_s_Treatment7_Strawberry_Cherry 0.85822235
## beta.Generation_Fruit_s_Treatment7_Strawberry_Cranberry 0.65218304
## beta.Generation_Fruit_s_Treatment7_Strawberry_Strawberry 0.69838567
## beta.Generation_Fruit_s_Treatment29_Cherry_Cherry 0.60878103
## beta.Generation_Fruit_s_Treatment29_Cherry_Cranberry 0.00613693
## beta.Generation_Fruit_s_Treatment29_Cherry_Strawberry 0.38074466
## beta.Generation_Fruit_s_Treatment29_Cranberry_Cherry 0.58915695
## beta.Generation_Fruit_s_Treatment29_Cranberry_Cranberry 0.51159660
## beta.Generation_Fruit_s_Treatment29_Cranberry_Strawberry -0.05311594
## beta.Generation_Fruit_s_Treatment29_Strawberry_Cherry 0.40559334
## beta.Generation_Fruit_s_Treatment29_Strawberry_Cranberry 0.51378585
## beta.Generation_Fruit_s_Treatment29_Strawberry_Strawberry 0.71092561
## sigma.Line 0.16899310## G0: no difference among different fruit media (beta.TreatmentrelCherry / beta.TreatmentrelCranberry include zero)
## G7: no fitness change for strawberry, cherry or cranberry populations
## G29: fitness increase for cherry populations on cherry medium, but not on strawberry or cranberry medium
## G29: fitness increase for cranberry populations on cranberry and cherry media, and fitness decrease on strawberry medium
## G29: strawberry population with no change on strawberry or cranberry medium and fitness decrease on cherry medium
# BACKUP
# mfitness_fruits <- lme4::glmer.nb(Nb_adults ~ -1 + Treatment + Fruit_s:Treatment:Generation
# + (1|Line), data=data)
# summary(mfitness_fruits)
# CIfitness <- confint(mfitness_fruits)3.5 Analysis fecundity
3.5.1 Compare fit of Poisson lognormal and negative binomial distributions
## Poisson lognormal model
mpoislognormal <- lme4::glmer(Nb_eggs ~ Generation*Fruit_s*Treatment + (1|Obs), family="poisson", data=data)
summary(mpoislognormal)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: Nb_eggs ~ Generation * Fruit_s * Treatment + (1 | Obs)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 17768.2 17887.5 -8862.1 17724.2 1652
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3733 -0.1321 0.0278 0.1410 0.4241
##
## Random effects:
## Groups Name Variance Std.Dev.
## Obs (Intercept) 0.1395 0.3735
## Number of obs: 1674, groups: Obs, 1674
##
## Fixed effects:
## Estimate Std. Error z value
## (Intercept) 4.952094 0.038337 129.172
## Generation29 -0.406577 0.056129 -7.244
## Generation7 0.249046 0.077600 3.209
## Fruit_sCranberry -0.021392 0.093958 -0.228
## Fruit_sStrawberry -0.051014 0.083309 -0.612
## TreatmentCranberry -0.209939 0.054382 -3.860
## TreatmentStrawberry -0.345191 0.054507 -6.333
## Generation29:Fruit_sCranberry 0.121143 0.107268 1.129
## Generation29:Fruit_sStrawberry -0.015555 0.101500 -0.153
## Generation29:TreatmentCranberry 0.447408 0.079279 5.643
## Generation7:TreatmentCranberry 0.029638 0.113040 0.262
## Generation29:TreatmentStrawberry 0.635715 0.079331 8.013
## Generation7:TreatmentStrawberry 0.236696 0.113721 2.081
## Fruit_sCranberry:TreatmentCranberry 0.116394 0.131984 0.882
## Fruit_sStrawberry:TreatmentCranberry 0.230489 0.121315 1.900
## Fruit_sCranberry:TreatmentStrawberry -0.196064 0.135897 -1.443
## Fruit_sStrawberry:TreatmentStrawberry 0.037818 0.120836 0.313
## Generation29:Fruit_sCranberry:TreatmentCranberry -0.243557 0.150783 -1.615
## Generation29:Fruit_sStrawberry:TreatmentCranberry -0.322735 0.146260 -2.207
## Generation29:Fruit_sCranberry:TreatmentStrawberry 0.034852 0.154195 0.226
## Generation29:Fruit_sStrawberry:TreatmentStrawberry 0.005531 0.145764 0.038
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## Generation29 4.37e-13 ***
## Generation7 0.001330 **
## Fruit_sCranberry 0.819895
## Fruit_sStrawberry 0.540308
## TreatmentCranberry 0.000113 ***
## TreatmentStrawberry 2.40e-10 ***
## Generation29:Fruit_sCranberry 0.258752
## Generation29:Fruit_sStrawberry 0.878199
## Generation29:TreatmentCranberry 1.67e-08 ***
## Generation7:TreatmentCranberry 0.793178
## Generation29:TreatmentStrawberry 1.12e-15 ***
## Generation7:TreatmentStrawberry 0.037401 *
## Fruit_sCranberry:TreatmentCranberry 0.377842
## Fruit_sStrawberry:TreatmentCranberry 0.057444 .
## Fruit_sCranberry:TreatmentStrawberry 0.149094
## Fruit_sStrawberry:TreatmentStrawberry 0.754302
## Generation29:Fruit_sCranberry:TreatmentCranberry 0.106250
## Generation29:Fruit_sStrawberry:TreatmentCranberry 0.027344 *
## Generation29:Fruit_sCranberry:TreatmentStrawberry 0.821183
## Generation29:Fruit_sStrawberry:TreatmentStrawberry 0.969729
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 15 columns / coefficients
## convergence code: 0
## Model failed to converge with max|grad| = 0.00706297 (tol = 0.002, component 1)## Check convergence
derivs1 <- mpoislognormal@optinfo$derivs
sc_grad1 <- with(derivs1,solve(Hessian,gradient))
max(abs(sc_grad1))## [1] 0.001258741## Negative binomial model
mnegbin <- MASS::glm.nb(Nb_eggs ~ Generation*Fruit_s*Treatment, data=data)
## Compare AIC
AIC(mnegbin, mpoislognormal)## df AIC
## mnegbin 22 17605.09
## mpoislognormal 22 17768.21## Simulate data with Poisson lognormal distribution
x.teo.poislognormal <- unlist(simulate(mpoislognormal))
x.teo.negbin <- unlist(simulate(mnegbin))
## QQplot to compared Negative binomial and Poisson log normal distributions
qqplot(data$Nb_eggs, x.teo.negbin, main="QQ-plot", xlab="Observed data", ylab="Simulated data", las=1, xlim = c(0, 100), ylim = c(0, 100)) ## QQ-plot
points(sort(data$Nb_eggs), sort(x.teo.poislognormal), pch=1, col='red')
abline(0,1)
## Add legend
legend(0, 70, legend=c("Neg. Bin.", "Pois. Lognormal"), col=c("black", "red"), pch=1, bty="n")
## Negative binomial distribution provides a better fit to the data3.5.2 Compare changes in fecundity
m0 <- MASS::glm.nb(Nb_eggs ~ Generation*Fruit_s, data=data)
summary(m0)##
## Call:
## MASS::glm.nb(formula = Nb_eggs ~ Generation * Fruit_s, data = data,
## init.theta = 7.377024136, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -5.7045 -0.7038 -0.0426 0.5555 3.0911
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.842033 0.021867 221.433 < 2e-16 ***
## Generation29 -0.055158 0.031798 -1.735 0.0828 .
## Generation7 0.364938 0.045817 7.965 1.65e-15 ***
## Fruit_sCranberry -0.060454 0.053593 -1.128 0.2593
## Fruit_sGF NA NA NA NA
## Fruit_sStrawberry 0.005185 0.048861 0.106 0.9155
## Generation29:Fruit_sCranberry 0.056394 0.061033 0.924 0.3555
## Generation7:Fruit_sCranberry NA NA NA NA
## Generation29:Fruit_sGF NA NA NA NA
## Generation7:Fruit_sGF NA NA NA NA
## Generation29:Fruit_sStrawberry -0.095729 0.058790 -1.628 0.1035
## Generation7:Fruit_sStrawberry NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(7.377) family taken to be 1)
##
## Null deviance: 2129.0 on 1673 degrees of freedom
## Residual deviance: 1728.7 on 1667 degrees of freedom
## AIC: 17690
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 7.377
## Std. Err.: 0.268
##
## 2 x log-likelihood: -17673.630m1 <- MASS::glm.nb(Nb_eggs ~ -1 + Generation_Fruit_s_Treatment, data=data)
summary(m1)##
## Call:
## MASS::glm.nb(formula = Nb_eggs ~ -1 + Generation_Fruit_s_Treatment,
## data = data, init.theta = 7.913400412, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -5.6762 -0.6836 -0.0461 0.5447 3.0625
##
## Coefficients:
## Estimate Std. Error
## Generation_Fruit_s_Treatment0_GF_Cherry 5.00723 0.03648
## Generation_Fruit_s_Treatment0_GF_Cranberry 4.81096 0.03668
## Generation_Fruit_s_Treatment0_GF_Strawberry 4.68065 0.03683
## Generation_Fruit_s_Treatment29_Cherry_Cherry 4.64214 0.03887
## Generation_Fruit_s_Treatment29_Cherry_Cranberry 4.82671 0.03864
## Generation_Fruit_s_Treatment29_Cherry_Strawberry 4.87681 0.03858
## Generation_Fruit_s_Treatment29_Cranberry_Cherry 4.70272 0.03005
## Generation_Fruit_s_Treatment29_Cranberry_Cranberry 4.81786 0.02994
## Generation_Fruit_s_Treatment29_Cranberry_Strawberry 4.82329 0.02993
## Generation_Fruit_s_Treatment29_Strawberry_Cherry 4.53391 0.03903
## Generation_Fruit_s_Treatment29_Strawberry_Cranberry 4.68542 0.03882
## Generation_Fruit_s_Treatment29_Strawberry_Strawberry 4.84541 0.03862
## Generation_Fruit_s_Treatment7_Cherry_Cherry 5.27604 0.06410
## Generation_Fruit_s_Treatment7_Cherry_Cranberry 5.16458 0.06868
## Generation_Fruit_s_Treatment7_Cherry_Strawberry 5.16436 0.06994
## Generation_Fruit_s_Treatment7_Cranberry_Cherry 5.22401 0.06225
## Generation_Fruit_s_Treatment7_Cranberry_Cranberry 5.19938 0.05475
## Generation_Fruit_s_Treatment7_Cranberry_Strawberry 4.98810 0.06169
## Generation_Fruit_s_Treatment7_Strawberry_Cherry 5.20931 0.04649
## Generation_Fruit_s_Treatment7_Strawberry_Cranberry 5.26848 0.04762
## Generation_Fruit_s_Treatment7_Strawberry_Strawberry 5.16188 0.04508
## z value Pr(>|z|)
## Generation_Fruit_s_Treatment0_GF_Cherry 137.27 <2e-16 ***
## Generation_Fruit_s_Treatment0_GF_Cranberry 131.18 <2e-16 ***
## Generation_Fruit_s_Treatment0_GF_Strawberry 127.09 <2e-16 ***
## Generation_Fruit_s_Treatment29_Cherry_Cherry 119.42 <2e-16 ***
## Generation_Fruit_s_Treatment29_Cherry_Cranberry 124.91 <2e-16 ***
## Generation_Fruit_s_Treatment29_Cherry_Strawberry 126.39 <2e-16 ***
## Generation_Fruit_s_Treatment29_Cranberry_Cherry 156.50 <2e-16 ***
## Generation_Fruit_s_Treatment29_Cranberry_Cranberry 160.92 <2e-16 ***
## Generation_Fruit_s_Treatment29_Cranberry_Strawberry 161.13 <2e-16 ***
## Generation_Fruit_s_Treatment29_Strawberry_Cherry 116.16 <2e-16 ***
## Generation_Fruit_s_Treatment29_Strawberry_Cranberry 120.71 <2e-16 ***
## Generation_Fruit_s_Treatment29_Strawberry_Strawberry 125.47 <2e-16 ***
## Generation_Fruit_s_Treatment7_Cherry_Cherry 82.31 <2e-16 ***
## Generation_Fruit_s_Treatment7_Cherry_Cranberry 75.19 <2e-16 ***
## Generation_Fruit_s_Treatment7_Cherry_Strawberry 73.84 <2e-16 ***
## Generation_Fruit_s_Treatment7_Cranberry_Cherry 83.92 <2e-16 ***
## Generation_Fruit_s_Treatment7_Cranberry_Cranberry 94.97 <2e-16 ***
## Generation_Fruit_s_Treatment7_Cranberry_Strawberry 80.86 <2e-16 ***
## Generation_Fruit_s_Treatment7_Strawberry_Cherry 112.06 <2e-16 ***
## Generation_Fruit_s_Treatment7_Strawberry_Cranberry 110.64 <2e-16 ***
## Generation_Fruit_s_Treatment7_Strawberry_Strawberry 114.50 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(7.9134) family taken to be 1)
##
## Null deviance: 878081.8 on 1674 degrees of freedom
## Residual deviance: 1728.5 on 1653 degrees of freedom
## AIC: 17605
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 7.913
## Std. Err.: 0.289
##
## 2 x log-likelihood: -17561.088m2 <- lme4::glmer.nb(Nb_eggs ~ -1 + Generation_Fruit_s_Treatment + (1|Line), data = data, verbose=TRUE)## th := est_theta(glmer(..)) = 8.025924 --> dev.= -2*logLik(.) = 17561.24
## 1: th= 3.952992494, dev=17870.35338397, beta[1]= 5.00722950
## 2: th= 16.29536564, dev=18018.18209852, beta[1]= 5.00722217
## 3: th= 1.647212859, dev=18883.11041099, beta[1]= 5.00708268
## 4: th= 7.299870710, dev=17565.88051763, beta[1]= 5.00724752
## 5: th= 7.485156715, dev=17563.37873585, beta[1]= 5.00723026
## 6: th= 8.010336468, dev=17561.19953493, beta[1]= 5.00722623
## 7: th= 7.915948240, dev=17561.08823172, beta[1]= 5.00722694
## 8: th= 7.912782631, dev=17561.08815853, beta[1]= 5.00722782
## 9: th= 7.913401651, dev=17561.08815300, beta[1]= 5.00723133
## 10: th= 7.913533786, dev=17561.08815316, beta[1]= 5.00723041
## 11: th= 7.913269518, dev=17561.08815315, beta[1]= 5.00723012
## 12: th= 7.913401651, dev=17561.08815293, beta[1]= 5.00723035summary(m2)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Negative Binomial(7.9134) ( log )
## Formula: Nb_eggs ~ -1 + Generation_Fruit_s_Treatment + (1 | Line)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 17607.1 17731.8 -8780.5 17561.1 1651
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6593 -0.6294 -0.0458 0.5804 4.2617
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line (Intercept) 2.343e-12 1.531e-06
## Number of obs: 1674, groups: Line, 26
##
## Fixed effects:
## Estimate Std. Error
## Generation_Fruit_s_Treatment0_GF_Cherry 5.00723 0.03648
## Generation_Fruit_s_Treatment0_GF_Cranberry 4.81097 0.03668
## Generation_Fruit_s_Treatment0_GF_Strawberry 4.68065 0.03683
## Generation_Fruit_s_Treatment29_Cherry_Cherry 4.64214 0.03887
## Generation_Fruit_s_Treatment29_Cherry_Cranberry 4.82671 0.03864
## Generation_Fruit_s_Treatment29_Cherry_Strawberry 4.87681 0.03858
## Generation_Fruit_s_Treatment29_Cranberry_Cherry 4.70272 0.03005
## Generation_Fruit_s_Treatment29_Cranberry_Cranberry 4.81786 0.02994
## Generation_Fruit_s_Treatment29_Cranberry_Strawberry 4.82329 0.02993
## Generation_Fruit_s_Treatment29_Strawberry_Cherry 4.53391 0.03903
## Generation_Fruit_s_Treatment29_Strawberry_Cranberry 4.68542 0.03882
## Generation_Fruit_s_Treatment29_Strawberry_Strawberry 4.84541 0.03862
## Generation_Fruit_s_Treatment7_Cherry_Cherry 5.27604 0.06410
## Generation_Fruit_s_Treatment7_Cherry_Cranberry 5.16458 0.06868
## Generation_Fruit_s_Treatment7_Cherry_Strawberry 5.16436 0.06994
## Generation_Fruit_s_Treatment7_Cranberry_Cherry 5.22401 0.06225
## Generation_Fruit_s_Treatment7_Cranberry_Cranberry 5.19938 0.05475
## Generation_Fruit_s_Treatment7_Cranberry_Strawberry 4.98810 0.06169
## Generation_Fruit_s_Treatment7_Strawberry_Cherry 5.20930 0.04649
## Generation_Fruit_s_Treatment7_Strawberry_Cranberry 5.26848 0.04762
## Generation_Fruit_s_Treatment7_Strawberry_Strawberry 5.16188 0.04508
## z value Pr(>|z|)
## Generation_Fruit_s_Treatment0_GF_Cherry 137.27 <2e-16 ***
## Generation_Fruit_s_Treatment0_GF_Cranberry 131.18 <2e-16 ***
## Generation_Fruit_s_Treatment0_GF_Strawberry 127.09 <2e-16 ***
## Generation_Fruit_s_Treatment29_Cherry_Cherry 119.42 <2e-16 ***
## Generation_Fruit_s_Treatment29_Cherry_Cranberry 124.91 <2e-16 ***
## Generation_Fruit_s_Treatment29_Cherry_Strawberry 126.39 <2e-16 ***
## Generation_Fruit_s_Treatment29_Cranberry_Cherry 156.50 <2e-16 ***
## Generation_Fruit_s_Treatment29_Cranberry_Cranberry 160.92 <2e-16 ***
## Generation_Fruit_s_Treatment29_Cranberry_Strawberry 161.13 <2e-16 ***
## Generation_Fruit_s_Treatment29_Strawberry_Cherry 116.16 <2e-16 ***
## Generation_Fruit_s_Treatment29_Strawberry_Cranberry 120.71 <2e-16 ***
## Generation_Fruit_s_Treatment29_Strawberry_Strawberry 125.47 <2e-16 ***
## Generation_Fruit_s_Treatment7_Cherry_Cherry 82.31 <2e-16 ***
## Generation_Fruit_s_Treatment7_Cherry_Cranberry 75.19 <2e-16 ***
## Generation_Fruit_s_Treatment7_Cherry_Strawberry 73.84 <2e-16 ***
## Generation_Fruit_s_Treatment7_Cranberry_Cherry 83.92 <2e-16 ***
## Generation_Fruit_s_Treatment7_Cranberry_Cranberry 94.97 <2e-16 ***
## Generation_Fruit_s_Treatment7_Cranberry_Strawberry 80.86 <2e-16 ***
## Generation_Fruit_s_Treatment7_Strawberry_Cherry 112.06 <2e-16 ***
## Generation_Fruit_s_Treatment7_Strawberry_Cranberry 110.64 <2e-16 ***
## Generation_Fruit_s_Treatment7_Strawberry_Strawberry 114.50 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## convergence code: 0
## boundary (singular) fit: see ?isSingularMuMIn::model.sel(m0, m1, m2)## Model selection table
## (Int) Frt_s Gnr Frt_s:Gnr Gnr_Frt_s_Trt family
## m1 + Negative Binomial(7.9134,log)
## m2 + Negative Binomial(7.9134,log)
## m0 4.842 + + + Negative Binomial(7.377,log)
## class init.theta link random df logLik AICc delta weight
## m1 negbin 7.91 log 22 -8780.544 17605.7 0.00 0.737
## m2 glmerMod L 23 -8780.544 17607.8 2.06 0.263
## m0 negbin 7.38 log 8 -8836.815 17689.7 84.02 0.000
## Models ranked by AICc(x)
## Random terms:
## L = '1 | Line'm3 <- lme4::glmer.nb(Nb_eggs ~ Generation*Fruit_s*Treatmentrel + (1|Line), data = data, verbose=TRUE)## th := est_theta(glmer(..)) = 8.025924 --> dev.= -2*logLik(.) = 17561.24
## 1: th= 3.952992508, dev=17870.35338114, beta[1]= 4.68064865
## 2: th= 16.29536569, dev=18018.18821701, beta[1]= 4.68039503
## 3: th= 1.647212864, dev=18883.11885152, beta[1]= 4.68114587
## 4: th= 7.299849738, dev=17565.88420808, beta[1]= 4.68135311
## 5: th= 7.485133796, dev=17563.37978383, beta[1]= 4.68079517
## 6: th= 8.011172818, dev=17561.20174286, beta[1]= 4.68066248
## 7: th= 7.915734061, dev=17561.08843739, beta[1]= 4.68063970
## 8: th= 7.912764007, dev=17561.08833842, beta[1]= 4.68061126
## 9: th= 7.912896131, dev=17561.08831283, beta[1]= 4.68065174
## 10: th= 7.914040567, dev=17561.08827176, beta[1]= 4.68052128
## 11: th= 7.914687382, dev=17561.08826426, beta[1]= 4.68063195
## 12: th= 7.914819538, dev=17561.08824761, beta[1]= 4.68064901
## 13: th= 7.915168842, dev=17561.08825232, beta[1]= 4.68060105
## 14: th= 7.914951697, dev=17561.08824134, beta[1]= 4.68060926
## 15: th= 7.914951697, dev=17561.08823881, beta[1]= 4.68062832summary(m3)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Negative Binomial(7.915) ( log )
## Formula: Nb_eggs ~ Generation * Fruit_s * Treatmentrel + (1 | Line)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 17607.1 17731.8 -8780.5 17561.1 1651
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6596 -0.6295 -0.0459 0.5804 4.2620
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line (Intercept) 1.123e-08 0.000106
## Number of obs: 1674, groups: Line, 26
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 4.680628 0.036826
## Generation29 0.196194 0.053336
## Generation7 0.483777 0.079041
## Fruit_sCranberry -0.176303 0.093253
## Fruit_sStrawberry -0.002522 0.083207
## TreatmentrelCherry 0.326621 0.051832
## TreatmentrelCranberry 0.130346 0.051971
## Generation29:Fruit_sCranberry 0.122761 0.105265
## Generation29:Fruit_sStrawberry -0.028881 0.099516
## Generation29:TreatmentrelCherry -0.561288 0.075406
## Generation7:TreatmentrelCherry -0.215013 0.108101
## Generation29:TreatmentrelCranberry -0.180438 0.075382
## Generation7:TreatmentrelCranberry -0.130205 0.110944
## Fruit_sCranberry:TreatmentrelCherry 0.124316 0.129147
## Fruit_sStrawberry:TreatmentrelCherry -0.064182 0.114858
## Fruit_sCranberry:TreatmentrelCranberry 0.211146 0.128098
## Fruit_sStrawberry:TreatmentrelCranberry 0.106461 0.117928
## Generation29:Fruit_sCranberry:TreatmentrelCherry -0.010215 0.146552
## Generation29:Fruit_sStrawberry:TreatmentrelCherry -0.012671 0.138588
## Generation29:Fruit_sCranberry:TreatmentrelCranberry -0.166474 0.145544
## Generation29:Fruit_sStrawberry:TreatmentrelCranberry -0.216371 0.141019
## z value Pr(>|z|)
## (Intercept) 127.101 < 2e-16 ***
## Generation29 3.678 0.000235 ***
## Generation7 6.121 9.32e-10 ***
## Fruit_sCranberry -1.891 0.058680 .
## Fruit_sStrawberry -0.030 0.975825
## TreatmentrelCherry 6.302 2.95e-10 ***
## TreatmentrelCranberry 2.508 0.012140 *
## Generation29:Fruit_sCranberry 1.166 0.243531
## Generation29:Fruit_sStrawberry -0.290 0.771648
## Generation29:TreatmentrelCherry -7.444 9.80e-14 ***
## Generation7:TreatmentrelCherry -1.989 0.046702 *
## Generation29:TreatmentrelCranberry -2.394 0.016681 *
## Generation7:TreatmentrelCranberry -1.174 0.240548
## Fruit_sCranberry:TreatmentrelCherry 0.963 0.335753
## Fruit_sStrawberry:TreatmentrelCherry -0.559 0.576301
## Fruit_sCranberry:TreatmentrelCranberry 1.648 0.099289 .
## Fruit_sStrawberry:TreatmentrelCranberry 0.903 0.366652
## Generation29:Fruit_sCranberry:TreatmentrelCherry -0.070 0.944429
## Generation29:Fruit_sStrawberry:TreatmentrelCherry -0.091 0.927153
## Generation29:Fruit_sCranberry:TreatmentrelCranberry -1.144 0.252704
## Generation29:Fruit_sStrawberry:TreatmentrelCranberry -1.534 0.124947
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 15 columns / coefficients
## convergence code: 0
## Model failed to converge with max|grad| = 0.010463 (tol = 0.002, component 1)## Get overdispersion parameter
getME(m2, "glmer.nb.theta")## [1] 7.913402## Check residuals for the different experimental populations
plot(m2, resid(.) ~ as.numeric(Line))
boo01 <- lme4::bootMer(m2, mySumm, nsim = 50, re.form = NA)
head(data.frame(boo01))## beta.Generation_Fruit_s_Treatment0_GF_Cherry
## 1 5.106916
## 2 5.001150
## 3 4.944991
## 4 4.985838
## 5 5.063119
## 6 4.991097
## beta.Generation_Fruit_s_Treatment0_GF_Cranberry
## 1 4.819816
## 2 4.828813
## 3 4.860688
## 4 4.806606
## 5 4.853945
## 6 4.828560
## beta.Generation_Fruit_s_Treatment0_GF_Strawberry
## 1 4.698800
## 2 4.687128
## 3 4.671117
## 4 4.680238
## 5 4.729294
## 6 4.669222
## beta.Generation_Fruit_s_Treatment7_Cherry_Cherry
## 1 0.2698853
## 2 0.2075909
## 3 0.4297513
## 4 0.3316380
## 5 0.2289434
## 6 0.2425714
## beta.Generation_Fruit_s_Treatment7_Cherry_Cranberry
## 1 0.3024620
## 2 0.1762346
## 3 0.2720822
## 4 0.3754494
## 5 0.2150209
## 6 0.3136414
## beta.Generation_Fruit_s_Treatment7_Cherry_Strawberry
## 1 0.4866014
## 2 0.4785324
## 3 0.5363836
## 4 0.4658433
## 5 0.5523293
## 6 0.4717353
## beta.Generation_Fruit_s_Treatment7_Cranberry_Cherry
## 1 0.1728936
## 2 0.1826684
## 3 0.2439969
## 4 0.2643595
## 5 0.1280866
## 6 0.3434372
## beta.Generation_Fruit_s_Treatment7_Cranberry_Cranberry
## 1 0.3177019
## 2 0.3241932
## 3 0.3375679
## 4 0.3647346
## 5 0.3702228
## 6 0.2935474
## beta.Generation_Fruit_s_Treatment7_Cranberry_Strawberry
## 1 0.2570153
## 2 0.2304509
## 3 0.3645957
## 4 0.2341865
## 5 0.1768198
## 6 0.2830625
## beta.Generation_Fruit_s_Treatment7_Strawberry_Cherry
## 1 0.0786434
## 2 0.2647675
## 3 0.2247322
## 4 0.2715666
## 5 0.1826883
## 6 0.1968905
## beta.Generation_Fruit_s_Treatment7_Strawberry_Cranberry
## 1 0.4600726
## 2 0.4213450
## 3 0.3919818
## 4 0.5258247
## 5 0.4273354
## 6 0.5332697
## beta.Generation_Fruit_s_Treatment7_Strawberry_Strawberry
## 1 0.4403463
## 2 0.5088651
## 3 0.4936399
## 4 0.5076107
## 5 0.4745148
## 6 0.4737480
## beta.Generation_Fruit_s_Treatment29_Cherry_Cherry
## 1 -0.4610163
## 2 -0.4168134
## 3 -0.3208788
## 4 -0.3941687
## 5 -0.3797401
## 6 -0.3397377
## beta.Generation_Fruit_s_Treatment29_Cherry_Cranberry
## 1 0.002602066
## 2 0.040649991
## 3 -0.022992731
## 4 -0.014541478
## 5 0.013119918
## 6 -0.011699760
## beta.Generation_Fruit_s_Treatment29_Cherry_Strawberry
## 1 0.2579438
## 2 0.2197259
## 3 0.2019047
## 4 0.2870659
## 5 0.1014584
## 6 0.1231541
## beta.Generation_Fruit_s_Treatment29_Cranberry_Cherry
## 1 -0.5004526
## 2 -0.2905894
## 3 -0.2310120
## 4 -0.2688588
## 5 -0.3517407
## 6 -0.2753697
## beta.Generation_Fruit_s_Treatment29_Cranberry_Cranberry
## 1 -0.041562318
## 2 -0.023661701
## 3 -0.041060016
## 4 0.035915827
## 5 0.025256894
## 6 0.009501766
## beta.Generation_Fruit_s_Treatment29_Cranberry_Strawberry
## 1 0.12741030
## 2 0.10265153
## 3 0.16281625
## 4 0.08162794
## 5 0.10300516
## 6 0.14537835
## beta.Generation_Fruit_s_Treatment29_Strawberry_Cherry
## 1 -0.5533592
## 2 -0.4818896
## 3 -0.3104549
## 4 -0.4255933
## 5 -0.5707660
## 6 -0.4625740
## beta.Generation_Fruit_s_Treatment29_Strawberry_Cranberry
## 1 -0.12269162
## 2 -0.11426361
## 3 -0.15134024
## 4 -0.07129806
## 5 -0.19182030
## 6 -0.17466625
## beta.Generation_Fruit_s_Treatment29_Strawberry_Strawberry sigma.Line
## 1 0.13123641 0.0006952132
## 2 0.12048085 0.0104045016
## 3 0.20328145 0.0070225498
## 4 0.09899855 0.0106039145
## 5 0.11590758 0.0006661946
## 6 0.22839030 0.0030376152## Extract CI
bCI_tab(boo01)## Estimate
## beta.Generation_Fruit_s_Treatment0_GF_Cherry 5.007230e+00
## beta.Generation_Fruit_s_Treatment0_GF_Cranberry 4.810966e+00
## beta.Generation_Fruit_s_Treatment0_GF_Strawberry 4.680649e+00
## beta.Generation_Fruit_s_Treatment7_Cherry_Cherry 2.688127e-01
## beta.Generation_Fruit_s_Treatment7_Cherry_Cranberry 3.536150e-01
## beta.Generation_Fruit_s_Treatment7_Cherry_Strawberry 4.837138e-01
## beta.Generation_Fruit_s_Treatment7_Cranberry_Cherry 2.167796e-01
## beta.Generation_Fruit_s_Treatment7_Cranberry_Cranberry 3.884097e-01
## beta.Generation_Fruit_s_Treatment7_Cranberry_Strawberry 3.074533e-01
## beta.Generation_Fruit_s_Treatment7_Strawberry_Cherry 2.020746e-01
## beta.Generation_Fruit_s_Treatment7_Strawberry_Cranberry 4.575152e-01
## beta.Generation_Fruit_s_Treatment7_Strawberry_Strawberry 4.812294e-01
## beta.Generation_Fruit_s_Treatment29_Cherry_Cherry -3.650875e-01
## beta.Generation_Fruit_s_Treatment29_Cherry_Cranberry 1.574628e-02
## beta.Generation_Fruit_s_Treatment29_Cherry_Strawberry 1.961566e-01
## beta.Generation_Fruit_s_Treatment29_Cranberry_Cherry -3.045093e-01
## beta.Generation_Fruit_s_Treatment29_Cranberry_Cranberry 6.893562e-03
## beta.Generation_Fruit_s_Treatment29_Cranberry_Strawberry 1.426389e-01
## beta.Generation_Fruit_s_Treatment29_Strawberry_Cherry -4.733171e-01
## beta.Generation_Fruit_s_Treatment29_Strawberry_Cranberry -1.255473e-01
## beta.Generation_Fruit_s_Treatment29_Strawberry_Strawberry 1.647607e-01
## sigma.Line 1.530717e-06
## X2.5..
## beta.Generation_Fruit_s_Treatment0_GF_Cherry 4.947951e+00
## beta.Generation_Fruit_s_Treatment0_GF_Cranberry 4.751041e+00
## beta.Generation_Fruit_s_Treatment0_GF_Strawberry 4.617101e+00
## beta.Generation_Fruit_s_Treatment7_Cherry_Cherry 1.524759e-01
## beta.Generation_Fruit_s_Treatment7_Cherry_Cranberry 1.879989e-01
## beta.Generation_Fruit_s_Treatment7_Cherry_Strawberry 3.592210e-01
## beta.Generation_Fruit_s_Treatment7_Cranberry_Cherry 9.597523e-02
## beta.Generation_Fruit_s_Treatment7_Cranberry_Cranberry 2.609444e-01
## beta.Generation_Fruit_s_Treatment7_Cranberry_Strawberry 9.222560e-02
## beta.Generation_Fruit_s_Treatment7_Strawberry_Cherry 8.750623e-02
## beta.Generation_Fruit_s_Treatment7_Strawberry_Cranberry 3.454663e-01
## beta.Generation_Fruit_s_Treatment7_Strawberry_Strawberry 3.394911e-01
## beta.Generation_Fruit_s_Treatment29_Cherry_Cherry -4.575049e-01
## beta.Generation_Fruit_s_Treatment29_Cherry_Cranberry -9.905060e-02
## beta.Generation_Fruit_s_Treatment29_Cherry_Strawberry 1.063414e-01
## beta.Generation_Fruit_s_Treatment29_Cranberry_Cherry -4.700836e-01
## beta.Generation_Fruit_s_Treatment29_Cranberry_Cranberry -8.865157e-02
## beta.Generation_Fruit_s_Treatment29_Cranberry_Strawberry 3.509046e-02
## beta.Generation_Fruit_s_Treatment29_Strawberry_Cherry -5.878579e-01
## beta.Generation_Fruit_s_Treatment29_Strawberry_Cranberry -2.192347e-01
## beta.Generation_Fruit_s_Treatment29_Strawberry_Strawberry 8.196792e-02
## sigma.Line 1.591909e-07
## X97.5..
## beta.Generation_Fruit_s_Treatment0_GF_Cherry 5.098040365
## beta.Generation_Fruit_s_Treatment0_GF_Cranberry 4.870099926
## beta.Generation_Fruit_s_Treatment0_GF_Strawberry 4.754038740
## beta.Generation_Fruit_s_Treatment7_Cherry_Cherry 0.417472891
## beta.Generation_Fruit_s_Treatment7_Cherry_Cranberry 0.518039463
## beta.Generation_Fruit_s_Treatment7_Cherry_Strawberry 0.613574392
## beta.Generation_Fruit_s_Treatment7_Cranberry_Cherry 0.340297232
## beta.Generation_Fruit_s_Treatment7_Cranberry_Cranberry 0.509452446
## beta.Generation_Fruit_s_Treatment7_Cranberry_Strawberry 0.484470016
## beta.Generation_Fruit_s_Treatment7_Strawberry_Cherry 0.338387856
## beta.Generation_Fruit_s_Treatment7_Strawberry_Cranberry 0.588625976
## beta.Generation_Fruit_s_Treatment7_Strawberry_Strawberry 0.586513897
## beta.Generation_Fruit_s_Treatment29_Cherry_Cherry -0.261868217
## beta.Generation_Fruit_s_Treatment29_Cherry_Cranberry 0.087462105
## beta.Generation_Fruit_s_Treatment29_Cherry_Strawberry 0.284127914
## beta.Generation_Fruit_s_Treatment29_Cranberry_Cherry -0.233208907
## beta.Generation_Fruit_s_Treatment29_Cranberry_Cranberry 0.053764665
## beta.Generation_Fruit_s_Treatment29_Cranberry_Strawberry 0.208085082
## beta.Generation_Fruit_s_Treatment29_Strawberry_Cherry -0.322942925
## beta.Generation_Fruit_s_Treatment29_Strawberry_Cranberry -0.005838017
## beta.Generation_Fruit_s_Treatment29_Strawberry_Strawberry 0.280833259
## sigma.Line 0.019433459## G0: no difference among different fruit media (beta.TreatmentrelCherry / beta.TreatmentrelCranberry include zero)
## G7: no fitness change for strawberry, cherry or cranberry populations
## G29: fitness increase for cherry populations on cherry and cranberry medium, but not on strawberry medium
## G29: fitness increase for cranberry populations on cranberry and cherry media, and fitness decrease on strawberry medium
## G29: strawberry population with no change on strawberry or cranberry medium and fitness decrease on cherry medium3.6 Analysis egg-to-adult viability
3.6.1 Compare changes in egg-to-adult viability
head(data)## Generation Treatment Line Fruit_s Nb_eggs Nb_adults SA Emergence_rate Obs
## 993 0 Cherry Anc GF 76 6 0 0.07894737 1
## 994 0 Cherry Anc GF 89 17 0 0.19101124 2
## 995 0 Cherry Anc GF 57 12 0 0.21052632 3
## 996 0 Cherry Anc GF 172 24 0 0.13953488 4
## 997 0 Cherry Anc GF 173 33 0 0.19075145 5
## 998 0 Cherry Anc GF 91 18 0 0.19780220 6
## Generation_Fruit_s_Treatment Line_Treatment Treatmentrel
## 993 0_GF_Cherry Anc_Cherry Cherry
## 994 0_GF_Cherry Anc_Cherry Cherry
## 995 0_GF_Cherry Anc_Cherry Cherry
## 996 0_GF_Cherry Anc_Cherry Cherry
## 997 0_GF_Cherry Anc_Cherry Cherry
## 998 0_GF_Cherry Anc_Cherry Cherrym0 <- glm(cbind(Nb_adults, Nb_eggs) ~ Generation*Fruit_s, data=data, family="binomial")
summary(m0)##
## Call:
## glm(formula = cbind(Nb_adults, Nb_eggs) ~ Generation * Fruit_s,
## family = "binomial", data = data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -10.9886 -2.7082 0.0541 2.3393 10.8108
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.56150 0.01232 -126.795 < 2e-16 ***
## Generation29 0.06881 0.01789 3.847 0.000120 ***
## Generation7 -0.36422 0.02543 -14.324 < 2e-16 ***
## Fruit_sCranberry 0.15561 0.02913 5.341 9.24e-08 ***
## Fruit_sGF NA NA NA NA
## Fruit_sStrawberry 0.38572 0.02574 14.984 < 2e-16 ***
## Generation29:Fruit_sCranberry -0.11918 0.03340 -3.568 0.000359 ***
## Generation7:Fruit_sCranberry NA NA NA NA
## Generation29:Fruit_sGF NA NA NA NA
## Generation7:Fruit_sGF NA NA NA NA
## Generation29:Fruit_sStrawberry -0.09466 0.03126 -3.028 0.002461 **
## Generation7:Fruit_sStrawberry NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 22957 on 1673 degrees of freedom
## Residual deviance: 21701 on 1667 degrees of freedom
## AIC: 29527
##
## Number of Fisher Scoring iterations: 4m1 <- glm(cbind(Nb_adults, Nb_eggs) ~ -1 + Generation_Fruit_s_Treatment, data=data, family="binomial")
summary(m1)##
## Call:
## glm(formula = cbind(Nb_adults, Nb_eggs) ~ -1 + Generation_Fruit_s_Treatment,
## family = "binomial", data = data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -10.7379 -2.3979 0.0021 2.1329 13.4566
##
## Coefficients:
## Estimate Std. Error
## Generation_Fruit_s_Treatment0_GF_Cherry -1.78875 0.02161
## Generation_Fruit_s_Treatment0_GF_Cranberry -1.54711 0.02154
## Generation_Fruit_s_Treatment0_GF_Strawberry -1.32619 0.02102
## Generation_Fruit_s_Treatment29_Cherry_Cherry -1.16777 0.02124
## Generation_Fruit_s_Treatment29_Cherry_Cranberry -1.88638 0.02600
## Generation_Fruit_s_Treatment29_Cherry_Strawberry -1.48678 0.02143
## Generation_Fruit_s_Treatment29_Cranberry_Cherry -1.25528 0.01651
## Generation_Fruit_s_Treatment29_Cranberry_Cranberry -1.34610 0.01615
## Generation_Fruit_s_Treatment29_Cranberry_Strawberry -1.83190 0.01971
## Generation_Fruit_s_Treatment29_Strawberry_Cherry -1.25217 0.02317
## Generation_Fruit_s_Treatment29_Strawberry_Cranberry -1.26587 0.02159
## Generation_Fruit_s_Treatment29_Strawberry_Strawberry -1.11624 0.01882
## Generation_Fruit_s_Treatment7_Cherry_Cherry -2.08248 0.03797
## Generation_Fruit_s_Treatment7_Cherry_Cranberry -2.03845 0.04209
## Generation_Fruit_s_Treatment7_Cherry_Strawberry -1.66114 0.03642
## Generation_Fruit_s_Treatment7_Cranberry_Cherry -2.04718 0.03722
## Generation_Fruit_s_Treatment7_Cranberry_Cranberry -1.81037 0.02987
## Generation_Fruit_s_Treatment7_Cranberry_Strawberry -1.46342 0.03220
## Generation_Fruit_s_Treatment7_Strawberry_Cherry -1.49135 0.02208
## Generation_Fruit_s_Treatment7_Strawberry_Cranberry -1.70626 0.02404
## Generation_Fruit_s_Treatment7_Strawberry_Strawberry -1.44195 0.02147
## z value Pr(>|z|)
## Generation_Fruit_s_Treatment0_GF_Cherry -82.77 <2e-16 ***
## Generation_Fruit_s_Treatment0_GF_Cranberry -71.84 <2e-16 ***
## Generation_Fruit_s_Treatment0_GF_Strawberry -63.08 <2e-16 ***
## Generation_Fruit_s_Treatment29_Cherry_Cherry -54.97 <2e-16 ***
## Generation_Fruit_s_Treatment29_Cherry_Cranberry -72.54 <2e-16 ***
## Generation_Fruit_s_Treatment29_Cherry_Strawberry -69.38 <2e-16 ***
## Generation_Fruit_s_Treatment29_Cranberry_Cherry -76.02 <2e-16 ***
## Generation_Fruit_s_Treatment29_Cranberry_Cranberry -83.33 <2e-16 ***
## Generation_Fruit_s_Treatment29_Cranberry_Strawberry -92.95 <2e-16 ***
## Generation_Fruit_s_Treatment29_Strawberry_Cherry -54.05 <2e-16 ***
## Generation_Fruit_s_Treatment29_Strawberry_Cranberry -58.63 <2e-16 ***
## Generation_Fruit_s_Treatment29_Strawberry_Strawberry -59.31 <2e-16 ***
## Generation_Fruit_s_Treatment7_Cherry_Cherry -54.84 <2e-16 ***
## Generation_Fruit_s_Treatment7_Cherry_Cranberry -48.43 <2e-16 ***
## Generation_Fruit_s_Treatment7_Cherry_Strawberry -45.61 <2e-16 ***
## Generation_Fruit_s_Treatment7_Cranberry_Cherry -55.00 <2e-16 ***
## Generation_Fruit_s_Treatment7_Cranberry_Cranberry -60.60 <2e-16 ***
## Generation_Fruit_s_Treatment7_Cranberry_Strawberry -45.45 <2e-16 ***
## Generation_Fruit_s_Treatment7_Strawberry_Cherry -67.53 <2e-16 ***
## Generation_Fruit_s_Treatment7_Strawberry_Cranberry -70.97 <2e-16 ***
## Generation_Fruit_s_Treatment7_Strawberry_Strawberry -67.16 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 142163 on 1674 degrees of freedom
## Residual deviance: 20076 on 1653 degrees of freedom
## AIC: 27930
##
## Number of Fisher Scoring iterations: 5m2 <- lme4::glmer(cbind(Nb_adults, Nb_eggs) ~ -1 + Generation_Fruit_s_Treatment + (1|Line), data = data, verbose=TRUE, family="binomial")## start par. = 1 fn = 27096.05
## At return
## eval: 23 fn: 27033.634 par: 0.180886
## (NM) 20: f = 27033.6 at 0.180886 -1.78875 -1.54711 -1.32619 -1.17115 -1.89452 -1.49178 -1.25176 -1.35733 -1.843 -1.26965 -1.29259 -1.14724 -2.0963 -2.07523 -1.7086 -1.89216 -1.69028 -1.30098 -1.55205 -1.7655 -1.49762
## (NM) 40: f = 27033.6 at 0.180886 -1.78875 -1.54711 -1.32619 -1.17115 -1.89452 -1.49178 -1.25176 -1.35733 -1.843 -1.26965 -1.29259 -1.14724 -2.0963 -2.07523 -1.7086 -1.89216 -1.69028 -1.30098 -1.55205 -1.7655 -1.49762
## (NM) 60: f = 27033.6 at 0.180886 -1.78875 -1.54711 -1.32619 -1.17115 -1.89452 -1.49178 -1.25176 -1.35733 -1.843 -1.26965 -1.29259 -1.14724 -2.0963 -2.07523 -1.7086 -1.89216 -1.69028 -1.30098 -1.55205 -1.7655 -1.49762
## (NM) 80: f = 27033.6 at 0.180886 -1.78875 -1.54711 -1.32619 -1.17115 -1.89452 -1.49178 -1.25176 -1.35733 -1.843 -1.26965 -1.29259 -1.14724 -2.0963 -2.07523 -1.7086 -1.89216 -1.69028 -1.30098 -1.55205 -1.7655 -1.49762
## (NM) 100: f = 27033.6 at 0.180886 -1.78875 -1.54711 -1.32619 -1.17115 -1.89452 -1.49178 -1.25176 -1.35733 -1.843 -1.26965 -1.29259 -1.14724 -2.0963 -2.07523 -1.7086 -1.89216 -1.69028 -1.30098 -1.55205 -1.7655 -1.49762
## (NM) 120: f = 27033.6 at 0.180886 -1.78875 -1.54711 -1.32619 -1.17115 -1.89452 -1.49178 -1.25176 -1.35733 -1.843 -1.26965 -1.29259 -1.14724 -2.0963 -2.07523 -1.7086 -1.89216 -1.69028 -1.30098 -1.55205 -1.7655 -1.49762
## (NM) 140: f = 27033.6 at 0.180886 -1.78875 -1.54711 -1.32619 -1.17115 -1.89452 -1.49178 -1.25176 -1.35733 -1.843 -1.26965 -1.29259 -1.14724 -2.0963 -2.07523 -1.7086 -1.89216 -1.69028 -1.30098 -1.55205 -1.7655 -1.49762
## (NM) 160: f = 27033.6 at 0.180886 -1.78875 -1.54711 -1.32619 -1.17115 -1.89452 -1.49178 -1.25176 -1.35733 -1.843 -1.26965 -1.29259 -1.14724 -2.0963 -2.07523 -1.7086 -1.89216 -1.69028 -1.30098 -1.55205 -1.7655 -1.49762
## (NM) 180: f = 27033.6 at 0.180886 -1.78875 -1.54711 -1.32619 -1.17115 -1.89452 -1.49178 -1.25176 -1.35733 -1.843 -1.26965 -1.29259 -1.14724 -2.0963 -2.07523 -1.7086 -1.89216 -1.69028 -1.30098 -1.55205 -1.7655 -1.49762
## (NM) 200: f = 27033.6 at 0.180886 -1.78875 -1.54711 -1.32619 -1.17115 -1.89452 -1.49178 -1.25176 -1.35733 -1.843 -1.26965 -1.29259 -1.14724 -2.0963 -2.07523 -1.7086 -1.89216 -1.69028 -1.30098 -1.55205 -1.7655 -1.49762
## (NM) 220: f = 27033.6 at 0.180886 -1.78875 -1.54711 -1.32619 -1.17115 -1.89452 -1.49178 -1.25176 -1.35733 -1.843 -1.26965 -1.29259 -1.14724 -2.0963 -2.07523 -1.7086 -1.89216 -1.69028 -1.30098 -1.55205 -1.7655 -1.49762
## (NM) 240: f = 27033.6 at 0.180886 -1.78875 -1.54711 -1.32619 -1.17115 -1.89452 -1.49178 -1.25176 -1.35733 -1.843 -1.26965 -1.29259 -1.14724 -2.0963 -2.07523 -1.7086 -1.89216 -1.69028 -1.30098 -1.55205 -1.7655 -1.49762
## (NM) 260: f = 27033.6 at 0.180886 -1.78875 -1.54711 -1.32619 -1.17115 -1.89452 -1.49178 -1.25176 -1.35733 -1.843 -1.26965 -1.29259 -1.14724 -2.0963 -2.07523 -1.7086 -1.89216 -1.69028 -1.30098 -1.55205 -1.7655 -1.49762
## (NM) 280: f = 27033.6 at 0.180886 -1.78875 -1.54711 -1.32619 -1.17115 -1.89452 -1.49178 -1.25176 -1.35733 -1.843 -1.26965 -1.29259 -1.14724 -2.0963 -2.07523 -1.7086 -1.89216 -1.69028 -1.30098 -1.55205 -1.7655 -1.49762
## (NM) 300: f = 27033.6 at 0.180886 -1.78875 -1.54711 -1.32619 -1.17115 -1.89452 -1.49178 -1.25176 -1.35733 -1.843 -1.26965 -1.29259 -1.14724 -2.0963 -2.07523 -1.7086 -1.89216 -1.69028 -1.30098 -1.55205 -1.7655 -1.49762
## (NM) 320: f = 27033.6 at 0.180886 -1.78875 -1.54711 -1.32619 -1.17115 -1.89452 -1.49178 -1.25176 -1.35733 -1.843 -1.26965 -1.29259 -1.14724 -2.0963 -2.07523 -1.7086 -1.89216 -1.69028 -1.30098 -1.55205 -1.7655 -1.49762
## (NM) 340: f = 27033.6 at 0.180987 -1.78849 -1.54692 -1.32605 -1.17115 -1.89458 -1.49174 -1.25174 -1.35728 -1.84295 -1.2696 -1.29249 -1.14723 -2.09669 -2.07553 -1.70901 -1.89256 -1.69057 -1.30124 -1.55195 -1.76546 -1.49751
## (NM) 360: f = 27033.6 at 0.181001 -1.78854 -1.54698 -1.3261 -1.17118 -1.8946 -1.49177 -1.25178 -1.35732 -1.84299 -1.26961 -1.29249 -1.14724 -2.09675 -2.07558 -1.70907 -1.89259 -1.69062 -1.30127 -1.55199 -1.76549 -1.49755
## (NM) 380: f = 27033.6 at 0.180897 -1.78898 -1.54736 -1.3264 -1.17146 -1.89491 -1.49211 -1.25202 -1.35761 -1.8433 -1.26966 -1.29274 -1.14743 -2.09713 -2.07591 -1.70941 -1.8927 -1.69087 -1.30143 -1.55228 -1.76566 -1.49784
## (NM) 400: f = 27033.6 at 0.180912 -1.78907 -1.54743 -1.3265 -1.17151 -1.89487 -1.49213 -1.25203 -1.35766 -1.84332 -1.26991 -1.29277 -1.14743 -2.09718 -2.07611 -1.7094 -1.89278 -1.6909 -1.30152 -1.55245 -1.76596 -1.49787
## (NM) 420: f = 27033.6 at 0.180934 -1.78917 -1.54756 -1.32662 -1.17159 -1.89497 -1.49221 -1.2521 -1.35773 -1.84338 -1.26997 -1.29284 -1.14751 -2.09727 -2.07623 -1.70952 -1.89289 -1.691 -1.30162 -1.55253 -1.76606 -1.49797
## (NM) 440: f = 27033.6 at 0.180828 -1.78902 -1.54735 -1.32651 -1.17143 -1.89481 -1.49209 -1.25199 -1.35758 -1.84326 -1.26984 -1.29276 -1.1474 -2.09712 -2.07599 -1.70937 -1.89279 -1.69088 -1.30147 -1.55228 -1.76578 -1.49789
## (NM) 460: f = 27033.6 at 0.180839 -1.78919 -1.54755 -1.32659 -1.17151 -1.89501 -1.4922 -1.25212 -1.35767 -1.84336 -1.26986 -1.29284 -1.14747 -2.09727 -2.07623 -1.70949 -1.89301 -1.69105 -1.30163 -1.55251 -1.76596 -1.49803
## (NM) 480: f = 27033.6 at 0.180889 -1.78913 -1.54752 -1.3266 -1.17146 -1.89487 -1.4921 -1.25206 -1.3576 -1.84333 -1.26982 -1.29275 -1.14742 -2.09723 -2.07615 -1.70941 -1.89289 -1.691 -1.30156 -1.55249 -1.76588 -1.49804
## (NM) 500: f = 27033.6 at 0.180855 -1.78909 -1.54744 -1.32654 -1.17147 -1.89487 -1.49212 -1.25202 -1.35761 -1.8433 -1.26986 -1.29278 -1.14743 -2.09717 -2.07608 -1.7094 -1.89285 -1.69094 -1.30152 -1.55236 -1.76584 -1.49794
## (NM) 520: f = 27033.6 at 0.180857 -1.78917 -1.54754 -1.32658 -1.17151 -1.89497 -1.49218 -1.25209 -1.35766 -1.84335 -1.26987 -1.29283 -1.14746 -2.09726 -2.0762 -1.70947 -1.89296 -1.69103 -1.30161 -1.55247 -1.76594 -1.49801
## (NM) 540: f = 27033.6 at 0.18087 -1.78919 -1.54755 -1.32661 -1.1715 -1.89493 -1.49217 -1.25209 -1.35766 -1.84336 -1.26992 -1.29284 -1.1475 -2.09727 -2.0762 -1.70944 -1.89291 -1.69103 -1.30161 -1.55247 -1.76594 -1.49801
## (NM) 560: f = 27033.6 at 0.18088 -1.78915 -1.54753 -1.3266 -1.17148 -1.8949 -1.49213 -1.25206 -1.35762 -1.84334 -1.26985 -1.29278 -1.14743 -2.09723 -2.07616 -1.70942 -1.8929 -1.691 -1.30158 -1.55247 -1.7659 -1.49802
## (NM) 580: f = 27033.6 at 0.180879 -1.78921 -1.54753 -1.32662 -1.17147 -1.89485 -1.49211 -1.25203 -1.35762 -1.84334 -1.26987 -1.2928 -1.14743 -2.09722 -2.07615 -1.70941 -1.89289 -1.69101 -1.3016 -1.55246 -1.76593 -1.49804
## (NM) 600: f = 27033.6 at 0.180879 -1.78921 -1.54755 -1.32663 -1.17149 -1.8949 -1.49215 -1.25207 -1.35764 -1.84336 -1.26987 -1.29281 -1.14746 -2.09726 -2.0762 -1.70946 -1.89293 -1.69102 -1.30162 -1.55252 -1.76599 -1.49805
## (NM) 620: f = 27033.6 at 0.180881 -1.7892 -1.54754 -1.32663 -1.17146 -1.89488 -1.49213 -1.25205 -1.35762 -1.84334 -1.26986 -1.29279 -1.14743 -2.09721 -2.07615 -1.70943 -1.89293 -1.69101 -1.30161 -1.55249 -1.76596 -1.49806
## (NM) 640: f = 27033.6 at 0.180885 -1.78922 -1.54754 -1.32663 -1.17146 -1.89487 -1.49213 -1.25204 -1.35761 -1.84334 -1.26985 -1.29279 -1.14741 -2.09721 -2.07615 -1.70942 -1.89292 -1.69101 -1.30162 -1.5525 -1.76599 -1.49808
## (NM) 660: f = 27033.6 at 0.180891 -1.78919 -1.54755 -1.32663 -1.17147 -1.89487 -1.49212 -1.25204 -1.35762 -1.84334 -1.26984 -1.29278 -1.14742 -2.09723 -2.07617 -1.70945 -1.89292 -1.69102 -1.30162 -1.5525 -1.76598 -1.49807
## (NM) 680: f = 27033.6 at 0.180891 -1.78918 -1.54754 -1.32663 -1.17143 -1.89484 -1.49209 -1.25202 -1.3576 -1.84332 -1.26984 -1.29277 -1.14739 -2.0972 -2.07614 -1.70942 -1.89293 -1.69099 -1.30163 -1.55252 -1.766 -1.49809
## (NM) 700: f = 27033.6 at 0.180898 -1.78919 -1.54756 -1.32663 -1.17143 -1.89484 -1.49209 -1.25202 -1.3576 -1.84332 -1.26983 -1.29277 -1.14741 -2.09723 -2.07617 -1.70945 -1.89294 -1.69102 -1.30163 -1.55255 -1.76601 -1.49811
## (NM) 720: f = 27033.6 at 0.180887 -1.78915 -1.54751 -1.32659 -1.17138 -1.89478 -1.49204 -1.25197 -1.35755 -1.84327 -1.26978 -1.29272 -1.14734 -2.09718 -2.07612 -1.70938 -1.89293 -1.69101 -1.30162 -1.55256 -1.76602 -1.49811
## (NM) 740: f = 27033.6 at 0.18088 -1.7892 -1.54758 -1.32664 -1.17139 -1.89482 -1.49205 -1.25199 -1.35758 -1.84329 -1.26982 -1.29274 -1.14738 -2.09723 -2.07616 -1.70944 -1.89299 -1.69108 -1.30169 -1.55264 -1.76612 -1.49818
## (NM) 760: f = 27033.6 at 0.180895 -1.78919 -1.54756 -1.32664 -1.17133 -1.89478 -1.492 -1.25197 -1.35755 -1.84325 -1.26978 -1.29272 -1.14735 -2.0972 -2.07612 -1.70939 -1.89295 -1.69104 -1.30167 -1.55264 -1.76611 -1.4982
## (NM) 780: f = 27033.6 at 0.180891 -1.78918 -1.54755 -1.32662 -1.17131 -1.89474 -1.49198 -1.25194 -1.35752 -1.84324 -1.26973 -1.29266 -1.1473 -2.09717 -2.0761 -1.70939 -1.89297 -1.69106 -1.30167 -1.55265 -1.76611 -1.49822
## (NM) 800: f = 27033.6 at 0.180887 -1.78917 -1.54754 -1.32661 -1.17129 -1.89472 -1.49195 -1.25192 -1.3575 -1.84322 -1.26971 -1.29264 -1.14728 -2.09716 -2.07608 -1.70937 -1.89297 -1.69107 -1.30168 -1.55267 -1.76612 -1.49823
## (NM) 820: f = 27033.6 at 0.180872 -1.78916 -1.54752 -1.3266 -1.17128 -1.89472 -1.49193 -1.25192 -1.35749 -1.8432 -1.26969 -1.29263 -1.14727 -2.09718 -2.07609 -1.70937 -1.89296 -1.69107 -1.30167 -1.55269 -1.76616 -1.49825
## (NM) 840: f = 27033.6 at 0.180872 -1.78916 -1.54752 -1.3266 -1.17128 -1.89472 -1.49193 -1.25192 -1.35749 -1.8432 -1.26969 -1.29263 -1.14727 -2.09718 -2.07609 -1.70937 -1.89296 -1.69107 -1.30167 -1.55269 -1.76616 -1.49825
## (NM) 860: f = 27033.6 at 0.180878 -1.78919 -1.54754 -1.32661 -1.1713 -1.89474 -1.49196 -1.25193 -1.35751 -1.84322 -1.26971 -1.29265 -1.14728 -2.09719 -2.07613 -1.70941 -1.89298 -1.69109 -1.30169 -1.55269 -1.76615 -1.49825
## (NM) 880: f = 27033.6 at 0.180875 -1.78916 -1.54752 -1.32659 -1.17127 -1.8947 -1.49193 -1.25191 -1.35748 -1.8432 -1.26971 -1.29263 -1.14727 -2.09718 -2.07611 -1.70938 -1.89298 -1.69107 -1.30168 -1.55269 -1.76616 -1.49824
## (NM) 900: f = 27033.6 at 0.180871 -1.78919 -1.54753 -1.32661 -1.17131 -1.89472 -1.49195 -1.25193 -1.3575 -1.84322 -1.26971 -1.29264 -1.14729 -2.09721 -2.07615 -1.70941 -1.89298 -1.69108 -1.30169 -1.55269 -1.76617 -1.49826
## (NM) 920: f = 27033.6 at 0.180875 -1.78918 -1.54754 -1.32662 -1.1713 -1.89472 -1.49196 -1.25193 -1.35751 -1.84322 -1.26972 -1.29265 -1.14729 -2.09722 -2.07616 -1.70944 -1.89299 -1.69109 -1.3017 -1.55271 -1.76617 -1.49827
## (NM) 940: f = 27033.6 at 0.180868 -1.78917 -1.54752 -1.32661 -1.17129 -1.89471 -1.49194 -1.25191 -1.35749 -1.8432 -1.2697 -1.29263 -1.14728 -2.09722 -2.07615 -1.70942 -1.89299 -1.69108 -1.30169 -1.55269 -1.76618 -1.49826
## (NM) 960: f = 27033.6 at 0.180874 -1.78917 -1.54752 -1.32659 -1.17128 -1.8947 -1.49193 -1.25191 -1.35749 -1.8432 -1.2697 -1.29263 -1.14727 -2.0972 -2.07613 -1.7094 -1.89298 -1.69107 -1.30168 -1.55269 -1.76617 -1.49825
## (NM) 980: f = 27033.6 at 0.180875 -1.78917 -1.54752 -1.3266 -1.17129 -1.89471 -1.49194 -1.25192 -1.35749 -1.84321 -1.2697 -1.29263 -1.14728 -2.09721 -2.07614 -1.70942 -1.89297 -1.69107 -1.30167 -1.55269 -1.76617 -1.49826
## (NM) 1000: f = 27033.6 at 0.180872 -1.78916 -1.54752 -1.3266 -1.17129 -1.89471 -1.49195 -1.25191 -1.35749 -1.8432 -1.2697 -1.29263 -1.14728 -2.09722 -2.07615 -1.70943 -1.89298 -1.69107 -1.30168 -1.55269 -1.76618 -1.49825
## (NM) 1020: f = 27033.6 at 0.180876 -1.78916 -1.54751 -1.32659 -1.17128 -1.8947 -1.49193 -1.25191 -1.35749 -1.8432 -1.26968 -1.29261 -1.14726 -2.09723 -2.07615 -1.70943 -1.89299 -1.69108 -1.30168 -1.5527 -1.76619 -1.49827
## (NM) 1040: f = 27033.6 at 0.180876 -1.78916 -1.54751 -1.32659 -1.17128 -1.8947 -1.49193 -1.25191 -1.35749 -1.8432 -1.26968 -1.29261 -1.14726 -2.09723 -2.07615 -1.70943 -1.89299 -1.69108 -1.30168 -1.5527 -1.76619 -1.49827
## (NM) 1060: f = 27033.6 at 0.180875 -1.78915 -1.5475 -1.32658 -1.17127 -1.89469 -1.49193 -1.2519 -1.35748 -1.84319 -1.26967 -1.2926 -1.14725 -2.09724 -2.07615 -1.70944 -1.89298 -1.69107 -1.30167 -1.5527 -1.76619 -1.49827
## (NM) 1080: f = 27033.6 at 0.180878 -1.78916 -1.54752 -1.32659 -1.17127 -1.8947 -1.49193 -1.2519 -1.35748 -1.84319 -1.26968 -1.29261 -1.14726 -2.09723 -2.07615 -1.70944 -1.89298 -1.69108 -1.30168 -1.55271 -1.76619 -1.49828
## (NM) 1100: f = 27033.6 at 0.180876 -1.78916 -1.54751 -1.32658 -1.17128 -1.89471 -1.49195 -1.25191 -1.35749 -1.8432 -1.26969 -1.29262 -1.14727 -2.09725 -2.07616 -1.70945 -1.89298 -1.69107 -1.30167 -1.55269 -1.76618 -1.49826
## (NM) 1120: f = 27033.6 at 0.180876 -1.78915 -1.54751 -1.32658 -1.17128 -1.8947 -1.49194 -1.25191 -1.35749 -1.8432 -1.26968 -1.29261 -1.14726 -2.09725 -2.07617 -1.70946 -1.89298 -1.69107 -1.30168 -1.5527 -1.76619 -1.49827
## (NM) 1140: f = 27033.6 at 0.180875 -1.78915 -1.54751 -1.32658 -1.17128 -1.8947 -1.49194 -1.25191 -1.35749 -1.8432 -1.26968 -1.29262 -1.14726 -2.09725 -2.07617 -1.70945 -1.89298 -1.69107 -1.30167 -1.55271 -1.76619 -1.49827
## (NM) 1160: f = 27033.6 at 0.180878 -1.78914 -1.54749 -1.32657 -1.17127 -1.89469 -1.49193 -1.2519 -1.35748 -1.84319 -1.26968 -1.29261 -1.14726 -2.09725 -2.07616 -1.70946 -1.89297 -1.69106 -1.30166 -1.5527 -1.76619 -1.49827
## (NM) 1180: f = 27033.6 at 0.180876 -1.78915 -1.5475 -1.32657 -1.17127 -1.8947 -1.49194 -1.25191 -1.35749 -1.84319 -1.26968 -1.29262 -1.14726 -2.09726 -2.07618 -1.70947 -1.89297 -1.69107 -1.30167 -1.55271 -1.76619 -1.49827
## (NM) 1200: f = 27033.6 at 0.180879 -1.78914 -1.54749 -1.32657 -1.17126 -1.89469 -1.49193 -1.2519 -1.35749 -1.84319 -1.26968 -1.29262 -1.14726 -2.09725 -2.07617 -1.70946 -1.89297 -1.69107 -1.30167 -1.55271 -1.76619 -1.49828
## (NM) 1220: f = 27033.6 at 0.18088 -1.78913 -1.54748 -1.32656 -1.17126 -1.89469 -1.49193 -1.2519 -1.35748 -1.84319 -1.26968 -1.29262 -1.14726 -2.09727 -2.07618 -1.70948 -1.89296 -1.69106 -1.30166 -1.55271 -1.76619 -1.49828
## (NM) 1240: f = 27033.6 at 0.18088 -1.78913 -1.54749 -1.32657 -1.17127 -1.89469 -1.49193 -1.2519 -1.35749 -1.84319 -1.26969 -1.29263 -1.14727 -2.09726 -2.07618 -1.70947 -1.89296 -1.69107 -1.30167 -1.55271 -1.76619 -1.49828
## (NM) 1260: f = 27033.6 at 0.180879 -1.78914 -1.54749 -1.32657 -1.17126 -1.89468 -1.49192 -1.2519 -1.35748 -1.84319 -1.26969 -1.29262 -1.14727 -2.09725 -2.07619 -1.70947 -1.89297 -1.69107 -1.30167 -1.55272 -1.7662 -1.49829
## (NM) 1280: f = 27033.6 at 0.18088 -1.78911 -1.54747 -1.32655 -1.17124 -1.89467 -1.49191 -1.25189 -1.35748 -1.84318 -1.26969 -1.29263 -1.14727 -2.09726 -2.07619 -1.70948 -1.89295 -1.69107 -1.30166 -1.55272 -1.7662 -1.49829
## (NM) 1300: f = 27033.6 at 0.180877 -1.78912 -1.54748 -1.32655 -1.17125 -1.89468 -1.49192 -1.2519 -1.35748 -1.84319 -1.2697 -1.29264 -1.14728 -2.09726 -2.07619 -1.70948 -1.89295 -1.69106 -1.30165 -1.55272 -1.7662 -1.49828
## (NM) 1320: f = 27033.6 at 0.180877 -1.78909 -1.54745 -1.32652 -1.17124 -1.89466 -1.4919 -1.25189 -1.35748 -1.84318 -1.2697 -1.29264 -1.14728 -2.09728 -2.0762 -1.70948 -1.89294 -1.69105 -1.30164 -1.55271 -1.76618 -1.49827
## (NM) 1340: f = 27033.6 at 0.180873 -1.78908 -1.54744 -1.32651 -1.17124 -1.89466 -1.4919 -1.25189 -1.35748 -1.84318 -1.2697 -1.29264 -1.14728 -2.09728 -2.07622 -1.7095 -1.89294 -1.69104 -1.30163 -1.55272 -1.76619 -1.49828
## (NM) 1360: f = 27033.6 at 0.180869 -1.78906 -1.54742 -1.3265 -1.17123 -1.89465 -1.49189 -1.25189 -1.35747 -1.84318 -1.26971 -1.29265 -1.14729 -2.09729 -2.07623 -1.70949 -1.89293 -1.69104 -1.30162 -1.55272 -1.76619 -1.49828
## (NM) 1380: f = 27033.6 at 0.180872 -1.78907 -1.54743 -1.3265 -1.17125 -1.89467 -1.49191 -1.2519 -1.35748 -1.84319 -1.26971 -1.29265 -1.14729 -2.0973 -2.07623 -1.7095 -1.89294 -1.69104 -1.30163 -1.55271 -1.76618 -1.49827
## (NM) 1400: f = 27033.6 at 0.180875 -1.78907 -1.54743 -1.32651 -1.17123 -1.89465 -1.49189 -1.25189 -1.35747 -1.84318 -1.26971 -1.29265 -1.14729 -2.09728 -2.07622 -1.70949 -1.89293 -1.69104 -1.30163 -1.55272 -1.7662 -1.49828
## (NM) 1420: f = 27033.6 at 0.180874 -1.78908 -1.54744 -1.32651 -1.17123 -1.89466 -1.49189 -1.25189 -1.35747 -1.84318 -1.26971 -1.29265 -1.14728 -2.09728 -2.07621 -1.70949 -1.89294 -1.69104 -1.30163 -1.55271 -1.76619 -1.49828
## (NM) 1440: f = 27033.6 at 0.180874 -1.78907 -1.54742 -1.3265 -1.17123 -1.89465 -1.49189 -1.25189 -1.35747 -1.84318 -1.26971 -1.29265 -1.14729 -2.09729 -2.07623 -1.7095 -1.89293 -1.69103 -1.30162 -1.55271 -1.76619 -1.49827
## (NM) 1460: f = 27033.6 at 0.180874 -1.78908 -1.54743 -1.32651 -1.17124 -1.89466 -1.4919 -1.2519 -1.35748 -1.84319 -1.26971 -1.29265 -1.14729 -2.09729 -2.07622 -1.70949 -1.89294 -1.69104 -1.30163 -1.55271 -1.76619 -1.49827
## (NM) 1480: f = 27033.6 at 0.180877 -1.78908 -1.54744 -1.32651 -1.17124 -1.89466 -1.4919 -1.2519 -1.35748 -1.84319 -1.26972 -1.29266 -1.1473 -2.09728 -2.07622 -1.70949 -1.89293 -1.69104 -1.30163 -1.55271 -1.76619 -1.49828
## (NM) 1500: f = 27033.6 at 0.180878 -1.78907 -1.54743 -1.3265 -1.17123 -1.89465 -1.49189 -1.2519 -1.35748 -1.84319 -1.26973 -1.29267 -1.1473 -2.09729 -2.07623 -1.70949 -1.89293 -1.69104 -1.30163 -1.55271 -1.76619 -1.49827
## (NM) 1520: f = 27033.6 at 0.180879 -1.78907 -1.54743 -1.3265 -1.17123 -1.89465 -1.49189 -1.2519 -1.35748 -1.84319 -1.26973 -1.29266 -1.1473 -2.09728 -2.07622 -1.70949 -1.89293 -1.69104 -1.30163 -1.55271 -1.76619 -1.49827
## (NM) 1540: f = 27033.6 at 0.180881 -1.78908 -1.54743 -1.32651 -1.17123 -1.89465 -1.49189 -1.2519 -1.35748 -1.84319 -1.26972 -1.29266 -1.1473 -2.09728 -2.07622 -1.70949 -1.89294 -1.69104 -1.30163 -1.55271 -1.76619 -1.49828summary(m2)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: cbind(Nb_adults, Nb_eggs) ~ -1 + Generation_Fruit_s_Treatment +
## (1 | Line)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 27077.6 27196.9 -13516.8 27033.6 1652
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.2174 -2.2315 0.0267 2.2417 17.5345
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line (Intercept) 0.03272 0.1809
## Number of obs: 1674, groups: Line, 26
##
## Fixed effects:
## Estimate Std. Error
## Generation_Fruit_s_Treatment0_GF_Cherry -1.78908 0.18217
## Generation_Fruit_s_Treatment0_GF_Cranberry -1.54743 0.18216
## Generation_Fruit_s_Treatment0_GF_Strawberry -1.32651 0.18210
## Generation_Fruit_s_Treatment29_Cherry_Cherry -1.17123 0.10658
## Generation_Fruit_s_Treatment29_Cherry_Cranberry -1.89465 0.10764
## Generation_Fruit_s_Treatment29_Cherry_Strawberry -1.49189 0.10662
## Generation_Fruit_s_Treatment29_Cranberry_Cherry -1.25190 0.08256
## Generation_Fruit_s_Treatment29_Cranberry_Cranberry -1.35748 0.08251
## Generation_Fruit_s_Treatment29_Cranberry_Strawberry -1.84319 0.08327
## Generation_Fruit_s_Treatment29_Strawberry_Cherry -1.26972 0.10702
## Generation_Fruit_s_Treatment29_Strawberry_Cranberry -1.29266 0.10670
## Generation_Fruit_s_Treatment29_Strawberry_Strawberry -1.14730 0.10616
## Generation_Fruit_s_Treatment7_Cherry_Cherry -2.09728 0.09867
## Generation_Fruit_s_Treatment7_Cherry_Cranberry -2.07622 0.10064
## Generation_Fruit_s_Treatment7_Cherry_Strawberry -1.70949 0.09882
## Generation_Fruit_s_Treatment7_Cranberry_Cherry -1.89293 0.09035
## Generation_Fruit_s_Treatment7_Cranberry_Cranberry -1.69104 0.08694
## Generation_Fruit_s_Treatment7_Cranberry_Strawberry -1.30163 0.08831
## Generation_Fruit_s_Treatment7_Strawberry_Cherry -1.55271 0.08592
## Generation_Fruit_s_Treatment7_Strawberry_Cranberry -1.76619 0.08620
## Generation_Fruit_s_Treatment7_Strawberry_Strawberry -1.49828 0.08545
## z value Pr(>|z|)
## Generation_Fruit_s_Treatment0_GF_Cherry -9.821 < 2e-16 ***
## Generation_Fruit_s_Treatment0_GF_Cranberry -8.495 < 2e-16 ***
## Generation_Fruit_s_Treatment0_GF_Strawberry -7.284 3.23e-13 ***
## Generation_Fruit_s_Treatment29_Cherry_Cherry -10.990 < 2e-16 ***
## Generation_Fruit_s_Treatment29_Cherry_Cranberry -17.602 < 2e-16 ***
## Generation_Fruit_s_Treatment29_Cherry_Strawberry -13.993 < 2e-16 ***
## Generation_Fruit_s_Treatment29_Cranberry_Cherry -15.163 < 2e-16 ***
## Generation_Fruit_s_Treatment29_Cranberry_Cranberry -16.453 < 2e-16 ***
## Generation_Fruit_s_Treatment29_Cranberry_Strawberry -22.134 < 2e-16 ***
## Generation_Fruit_s_Treatment29_Strawberry_Cherry -11.865 < 2e-16 ***
## Generation_Fruit_s_Treatment29_Strawberry_Cranberry -12.115 < 2e-16 ***
## Generation_Fruit_s_Treatment29_Strawberry_Strawberry -10.807 < 2e-16 ***
## Generation_Fruit_s_Treatment7_Cherry_Cherry -21.256 < 2e-16 ***
## Generation_Fruit_s_Treatment7_Cherry_Cranberry -20.631 < 2e-16 ***
## Generation_Fruit_s_Treatment7_Cherry_Strawberry -17.300 < 2e-16 ***
## Generation_Fruit_s_Treatment7_Cranberry_Cherry -20.950 < 2e-16 ***
## Generation_Fruit_s_Treatment7_Cranberry_Cranberry -19.450 < 2e-16 ***
## Generation_Fruit_s_Treatment7_Cranberry_Strawberry -14.740 < 2e-16 ***
## Generation_Fruit_s_Treatment7_Strawberry_Cherry -18.071 < 2e-16 ***
## Generation_Fruit_s_Treatment7_Strawberry_Cranberry -20.490 < 2e-16 ***
## Generation_Fruit_s_Treatment7_Strawberry_Strawberry -17.533 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1MuMIn::model.sel(m0, m1, m2)## Model selection table
## (Int) Frt_s Gnr Frt_s:Gnr Gnr_Frt_s_Trt family class verbose
## m2 + binomial(logit) glmerMod TRUE
## m1 + binomial(logit) glm
## m0 -1.561 + + + binomial(logit) glm
## random df logLik AICc delta weight
## m2 L 22 -13516.82 27078.2 0.00 1
## m1 21 -13943.84 27930.2 851.99 0
## m0 7 -14756.38 29526.8 2448.59 0
## Models ranked by AICc(x)
## Random terms:
## L = '1 | Line'## Check residuals for the different experimental populations
plot(m2, resid(.) ~ as.numeric(Line))
boo01 <- lme4::bootMer(m2, mySumm, nsim = 50, re.form = NA)
head(data.frame(boo01))## beta.Generation_Fruit_s_Treatment0_GF_Cherry
## 1 -1.743725
## 2 -1.865371
## 3 -1.851976
## 4 -1.724574
## 5 -1.756578
## 6 -1.817264
## beta.Generation_Fruit_s_Treatment0_GF_Cranberry
## 1 -1.541776
## 2 -1.618555
## 3 -1.597629
## 4 -1.517730
## 5 -1.499889
## 6 -1.582096
## beta.Generation_Fruit_s_Treatment0_GF_Strawberry
## 1 -1.319072
## 2 -1.373358
## 3 -1.391124
## 4 -1.319846
## 5 -1.284999
## 6 -1.362666
## beta.Generation_Fruit_s_Treatment7_Cherry_Cherry
## 1 -0.2953520
## 2 -0.2689570
## 3 -0.2474043
## 4 -0.2787308
## 5 -0.5268370
## 6 -0.4000189
## beta.Generation_Fruit_s_Treatment7_Cherry_Cranberry
## 1 -0.5092523
## 2 -0.5265649
## 3 -0.4691874
## 4 -0.4453035
## 5 -0.7473835
## 6 -0.5908984
## beta.Generation_Fruit_s_Treatment7_Cherry_Strawberry
## 1 -0.3954085
## 2 -0.4058025
## 3 -0.3697334
## 4 -0.2345346
## 5 -0.6268052
## 6 -0.4430149
## beta.Generation_Fruit_s_Treatment7_Cranberry_Cherry
## 1 -0.1976273
## 2 0.1641529
## 3 -0.0614760
## 4 -0.2039432
## 5 -0.1383276
## 6 -0.1386298
## beta.Generation_Fruit_s_Treatment7_Cranberry_Cranberry
## 1 -0.2139803
## 2 0.1260060
## 3 -0.0541312
## 4 -0.2142183
## 5 -0.2455216
## 6 -0.1824070
## beta.Generation_Fruit_s_Treatment7_Cranberry_Strawberry
## 1 -0.09334005
## 2 0.28528263
## 3 0.10173848
## 4 -0.01405573
## 5 0.01848418
## 6 0.05928137
## beta.Generation_Fruit_s_Treatment7_Strawberry_Cherry
## 1 0.1488840
## 2 0.4409035
## 3 0.4516813
## 4 0.1379182
## 5 0.2564592
## 6 0.1627865
## beta.Generation_Fruit_s_Treatment7_Strawberry_Cranberry
## 1 -0.26837873
## 2 -0.03155987
## 3 -0.05159086
## 4 -0.26900056
## 5 -0.16997340
## 6 -0.23538190
## beta.Generation_Fruit_s_Treatment7_Strawberry_Strawberry
## 1 -0.225729710
## 2 -0.002394596
## 3 0.035139939
## 4 -0.189858442
## 5 -0.131298656
## 6 -0.240091554
## beta.Generation_Fruit_s_Treatment29_Cherry_Cherry
## 1 0.5915223
## 2 0.6954692
## 3 0.5366219
## 4 0.4967005
## 5 0.5974800
## 6 0.7289909
## beta.Generation_Fruit_s_Treatment29_Cherry_Cranberry
## 1 -0.3390496
## 2 -0.2998871
## 3 -0.5274240
## 4 -0.4534270
## 5 -0.3953031
## 6 -0.2157260
## beta.Generation_Fruit_s_Treatment29_Cherry_Strawberry
## 1 -0.18574860
## 2 -0.11708914
## 3 -0.25177242
## 4 -0.22855351
## 5 -0.20126650
## 6 -0.07124183
## beta.Generation_Fruit_s_Treatment29_Cranberry_Cherry
## 1 0.5401418
## 2 0.5865537
## 3 0.6951295
## 4 0.4228175
## 5 0.5837039
## 6 0.6808567
## beta.Generation_Fruit_s_Treatment29_Cranberry_Cranberry
## 1 0.2625346
## 2 0.2272899
## 3 0.3474279
## 4 0.1292057
## 5 0.1853206
## 6 0.3435737
## beta.Generation_Fruit_s_Treatment29_Cranberry_Strawberry
## 1 -0.4732706
## 2 -0.4625041
## 3 -0.3664613
## 4 -0.5319951
## 5 -0.4877154
## 6 -0.3482399
## beta.Generation_Fruit_s_Treatment29_Strawberry_Cherry
## 1 0.5143357
## 2 0.4746184
## 3 0.6641229
## 4 0.4963949
## 5 0.5150906
## 6 0.6177252
## beta.Generation_Fruit_s_Treatment29_Strawberry_Cranberry
## 1 0.2874851
## 2 0.1453642
## 3 0.3661118
## 4 0.2299310
## 5 0.2548419
## 6 0.3832180
## beta.Generation_Fruit_s_Treatment29_Strawberry_Strawberry sigma.Line
## 1 0.22886508 0.1461729
## 2 0.04712573 0.1328527
## 3 0.31334466 0.1528380
## 4 0.21095589 0.1635870
## 5 0.17577524 0.1749053
## 6 0.29186712 0.1491875## Extract CI
bCI_tab(boo01)## Estimate
## beta.Generation_Fruit_s_Treatment0_GF_Cherry -1.7890786
## beta.Generation_Fruit_s_Treatment0_GF_Cranberry -1.5474332
## beta.Generation_Fruit_s_Treatment0_GF_Strawberry -1.3265083
## beta.Generation_Fruit_s_Treatment7_Cherry_Cherry -0.3082037
## beta.Generation_Fruit_s_Treatment7_Cherry_Cranberry -0.5287878
## beta.Generation_Fruit_s_Treatment7_Cherry_Strawberry -0.3829831
## beta.Generation_Fruit_s_Treatment7_Cranberry_Cherry -0.1038564
## beta.Generation_Fruit_s_Treatment7_Cranberry_Cranberry -0.1436100
## beta.Generation_Fruit_s_Treatment7_Cranberry_Strawberry 0.0248736
## beta.Generation_Fruit_s_Treatment7_Strawberry_Cherry 0.2363712
## beta.Generation_Fruit_s_Treatment7_Strawberry_Cranberry -0.2187578
## beta.Generation_Fruit_s_Treatment7_Strawberry_Strawberry -0.1717675
## beta.Generation_Fruit_s_Treatment29_Cherry_Cherry 0.6178465
## beta.Generation_Fruit_s_Treatment29_Cherry_Cranberry -0.3472205
## beta.Generation_Fruit_s_Treatment29_Cherry_Strawberry -0.1653853
## beta.Generation_Fruit_s_Treatment29_Cranberry_Cherry 0.5371816
## beta.Generation_Fruit_s_Treatment29_Cranberry_Cranberry 0.1899540
## beta.Generation_Fruit_s_Treatment29_Cranberry_Strawberry -0.5166812
## beta.Generation_Fruit_s_Treatment29_Strawberry_Cherry 0.5193550
## beta.Generation_Fruit_s_Treatment29_Strawberry_Cranberry 0.2547695
## beta.Generation_Fruit_s_Treatment29_Strawberry_Strawberry 0.1792083
## sigma.Line 0.1808808
## X2.5..
## beta.Generation_Fruit_s_Treatment0_GF_Cherry -2.00515081
## beta.Generation_Fruit_s_Treatment0_GF_Cranberry -1.68105789
## beta.Generation_Fruit_s_Treatment0_GF_Strawberry -1.48081566
## beta.Generation_Fruit_s_Treatment7_Cherry_Cherry -0.52549034
## beta.Generation_Fruit_s_Treatment7_Cherry_Cranberry -0.82725255
## beta.Generation_Fruit_s_Treatment7_Cherry_Strawberry -0.61556496
## beta.Generation_Fruit_s_Treatment7_Cranberry_Cherry -0.45928385
## beta.Generation_Fruit_s_Treatment7_Cranberry_Cranberry -0.50743713
## beta.Generation_Fruit_s_Treatment7_Cranberry_Strawberry -0.32180494
## beta.Generation_Fruit_s_Treatment7_Strawberry_Cherry -0.13708834
## beta.Generation_Fruit_s_Treatment7_Strawberry_Cranberry -0.57839874
## beta.Generation_Fruit_s_Treatment7_Strawberry_Strawberry -0.53240263
## beta.Generation_Fruit_s_Treatment29_Cherry_Cherry 0.35482159
## beta.Generation_Fruit_s_Treatment29_Cherry_Cranberry -0.62779722
## beta.Generation_Fruit_s_Treatment29_Cherry_Strawberry -0.42551499
## beta.Generation_Fruit_s_Treatment29_Cranberry_Cherry 0.24666697
## beta.Generation_Fruit_s_Treatment29_Cranberry_Cranberry -0.07561325
## beta.Generation_Fruit_s_Treatment29_Cranberry_Strawberry -0.78684179
## beta.Generation_Fruit_s_Treatment29_Strawberry_Cherry 0.25788694
## beta.Generation_Fruit_s_Treatment29_Strawberry_Cranberry -0.03882628
## beta.Generation_Fruit_s_Treatment29_Strawberry_Strawberry -0.07259799
## sigma.Line 0.10842845
## X97.5..
## beta.Generation_Fruit_s_Treatment0_GF_Cherry -1.56273122
## beta.Generation_Fruit_s_Treatment0_GF_Cranberry -1.32264298
## beta.Generation_Fruit_s_Treatment0_GF_Strawberry -1.11404225
## beta.Generation_Fruit_s_Treatment7_Cherry_Cherry -0.05547379
## beta.Generation_Fruit_s_Treatment7_Cherry_Cranberry -0.22127165
## beta.Generation_Fruit_s_Treatment7_Cherry_Strawberry -0.11239003
## beta.Generation_Fruit_s_Treatment7_Cranberry_Cherry 0.26407000
## beta.Generation_Fruit_s_Treatment7_Cranberry_Cranberry 0.23133315
## beta.Generation_Fruit_s_Treatment7_Cranberry_Strawberry 0.42551421
## beta.Generation_Fruit_s_Treatment7_Strawberry_Cherry 0.45590341
## beta.Generation_Fruit_s_Treatment7_Strawberry_Cranberry -0.03784079
## beta.Generation_Fruit_s_Treatment7_Strawberry_Strawberry 0.02296894
## beta.Generation_Fruit_s_Treatment29_Cherry_Cherry 0.92373014
## beta.Generation_Fruit_s_Treatment29_Cherry_Cranberry -0.12156588
## beta.Generation_Fruit_s_Treatment29_Cherry_Strawberry 0.09176605
## beta.Generation_Fruit_s_Treatment29_Cranberry_Cherry 0.86224582
## beta.Generation_Fruit_s_Treatment29_Cranberry_Cranberry 0.44781044
## beta.Generation_Fruit_s_Treatment29_Cranberry_Strawberry -0.23807699
## beta.Generation_Fruit_s_Treatment29_Strawberry_Cherry 0.78225259
## beta.Generation_Fruit_s_Treatment29_Strawberry_Cranberry 0.50173417
## beta.Generation_Fruit_s_Treatment29_Strawberry_Strawberry 0.43955544
## sigma.Line 0.20692044## G0: no difference among different fruit media (beta.TreatmentrelCherry / beta.TreatmentrelCranberry include zero)
## G7: no fitness change for strawberry, cherry or cranberry populations
## G29: fitness increase for cherry populations on cherry and cranberry medium, but not on strawberry medium
## G29: fitness increase for cranberry populations on cranberry and cherry media, and fitness decrease on strawberry medium
## G29: strawberry population with no change on strawberry or cranberry medium and fitness decrease on cherry medium
m3 <- lme4::glmer(cbind(Nb_adults, Nb_eggs) ~ -1 + Generation*Fruit_s*Treatmentrel + (1|Line), data = data, verbose=TRUE, family="binomial")## start par. = 1 fn = 27096.05
## At return
## eval: 23 fn: 27033.634 par: 0.180886
## (NM) 20: f = 27033.6 at 0.180886 -1.32619 -1.49178 -1.7086 0.407622 0.210982 -0.46256 -0.220921 -0.758842 0.133563 0.783194 0.0748633 -0.181812 -0.145712 -0.203486 0.333268 -0.0226744 0.0987452 0.4741 -0.776315 0.911084 0.15864
## (NM) 40: f = 27033.6 at 0.180886 -1.32619 -1.49178 -1.7086 0.407622 0.210982 -0.46256 -0.220921 -0.758842 0.133563 0.783194 0.0748633 -0.181812 -0.145712 -0.203486 0.333268 -0.0226744 0.0987452 0.4741 -0.776315 0.911084 0.15864
## (NM) 60: f = 27033.6 at 0.180886 -1.32619 -1.49178 -1.7086 0.407622 0.210982 -0.46256 -0.220921 -0.758842 0.133563 0.783194 0.0748633 -0.181812 -0.145712 -0.203486 0.333268 -0.0226744 0.0987452 0.4741 -0.776315 0.911084 0.15864
## (NM) 80: f = 27033.6 at 0.180886 -1.32619 -1.49178 -1.7086 0.407622 0.210982 -0.46256 -0.220921 -0.758842 0.133563 0.783194 0.0748633 -0.181812 -0.145712 -0.203486 0.333268 -0.0226744 0.0987452 0.4741 -0.776315 0.911084 0.15864
## (NM) 100: f = 27033.6 at 0.180886 -1.32619 -1.49178 -1.7086 0.407622 0.210982 -0.46256 -0.220921 -0.758842 0.133563 0.783194 0.0748633 -0.181812 -0.145712 -0.203486 0.333268 -0.0226744 0.0987452 0.4741 -0.776315 0.911084 0.15864
## (NM) 120: f = 27033.6 at 0.180886 -1.32619 -1.49178 -1.7086 0.407622 0.210982 -0.46256 -0.220921 -0.758842 0.133563 0.783194 0.0748633 -0.181812 -0.145712 -0.203486 0.333268 -0.0226744 0.0987452 0.4741 -0.776315 0.911084 0.15864
## (NM) 140: f = 27033.6 at 0.180886 -1.32619 -1.49178 -1.7086 0.407622 0.210982 -0.46256 -0.220921 -0.758842 0.133563 0.783194 0.0748633 -0.181812 -0.145712 -0.203486 0.333268 -0.0226744 0.0987452 0.4741 -0.776315 0.911084 0.15864
## (NM) 160: f = 27033.6 at 0.180886 -1.32619 -1.49178 -1.7086 0.407622 0.210982 -0.46256 -0.220921 -0.758842 0.133563 0.783194 0.0748633 -0.181812 -0.145712 -0.203486 0.333268 -0.0226744 0.0987452 0.4741 -0.776315 0.911084 0.15864
## (NM) 180: f = 27033.6 at 0.180886 -1.32619 -1.49178 -1.7086 0.407622 0.210982 -0.46256 -0.220921 -0.758842 0.133563 0.783194 0.0748633 -0.181812 -0.145712 -0.203486 0.333268 -0.0226744 0.0987452 0.4741 -0.776315 0.911084 0.15864
## (NM) 200: f = 27033.6 at 0.180886 -1.32619 -1.49178 -1.7086 0.407622 0.210982 -0.46256 -0.220921 -0.758842 0.133563 0.783194 0.0748633 -0.181812 -0.145712 -0.203486 0.333268 -0.0226744 0.0987452 0.4741 -0.776315 0.911084 0.15864
## (NM) 220: f = 27033.6 at 0.180886 -1.32619 -1.49178 -1.7086 0.407622 0.210982 -0.46256 -0.220921 -0.758842 0.133563 0.783194 0.0748633 -0.181812 -0.145712 -0.203486 0.333268 -0.0226744 0.0987452 0.4741 -0.776315 0.911084 0.15864
## (NM) 240: f = 27033.6 at 0.180886 -1.32619 -1.49178 -1.7086 0.407622 0.210982 -0.46256 -0.220921 -0.758842 0.133563 0.783194 0.0748633 -0.181812 -0.145712 -0.203486 0.333268 -0.0226744 0.0987452 0.4741 -0.776315 0.911084 0.15864
## (NM) 260: f = 27033.6 at 0.180886 -1.32619 -1.49178 -1.7086 0.407622 0.210982 -0.46256 -0.220921 -0.758842 0.133563 0.783194 0.0748633 -0.181812 -0.145712 -0.203486 0.333268 -0.0226744 0.0987452 0.4741 -0.776315 0.911084 0.15864
## (NM) 280: f = 27033.6 at 0.180886 -1.32619 -1.49178 -1.7086 0.407622 0.210982 -0.46256 -0.220921 -0.758842 0.133563 0.783194 0.0748633 -0.181812 -0.145712 -0.203486 0.333268 -0.0226744 0.0987452 0.4741 -0.776315 0.911084 0.15864
## (NM) 300: f = 27033.6 at 0.180886 -1.32619 -1.49178 -1.7086 0.407622 0.210982 -0.46256 -0.220921 -0.758842 0.133563 0.783194 0.0748633 -0.181812 -0.145712 -0.203486 0.333268 -0.0226744 0.0987452 0.4741 -0.776315 0.911084 0.15864
## (NM) 320: f = 27033.6 at 0.180886 -1.32619 -1.49178 -1.7086 0.407622 0.210982 -0.46256 -0.220921 -0.758842 0.133563 0.783194 0.0748633 -0.181812 -0.145712 -0.203486 0.333268 -0.0226744 0.0987452 0.4741 -0.776315 0.911084 0.15864
## (NM) 340: f = 27033.6 at 0.180886 -1.32619 -1.49178 -1.7086 0.407622 0.210982 -0.46256 -0.220921 -0.758842 0.133563 0.783194 0.0748633 -0.181812 -0.145712 -0.203486 0.333268 -0.0226744 0.0987452 0.4741 -0.776315 0.911084 0.15864
## (NM) 360: f = 27033.6 at 0.180886 -1.32619 -1.49178 -1.7086 0.407622 0.210982 -0.46256 -0.220921 -0.758842 0.133563 0.783194 0.0748633 -0.181812 -0.145712 -0.203486 0.333268 -0.0226744 0.0987452 0.4741 -0.776315 0.911084 0.15864
## (NM) 380: f = 27033.6 at 0.180886 -1.32619 -1.49178 -1.7086 0.407622 0.210982 -0.46256 -0.220921 -0.758842 0.133563 0.783194 0.0748633 -0.181812 -0.145712 -0.203486 0.333268 -0.0226744 0.0987452 0.4741 -0.776315 0.911084 0.15864
## (NM) 400: f = 27033.6 at 0.180975 -1.32567 -1.49179 -1.70908 0.407121 0.211137 -0.462678 -0.220981 -0.758819 0.133482 0.783267 0.0751058 -0.181821 -0.145586 -0.203671 0.332923 -0.0226064 0.0985232 0.47445 -0.775846 0.911238 0.15907
## (NM) 420: f = 27033.6 at 0.180802 -1.32625 -1.49193 -1.70917 0.407163 0.211173 -0.462685 -0.22092 -0.758621 0.133159 0.783288 0.075011 -0.181814 -0.145684 -0.203698 0.333125 -0.022736 0.0985276 0.474286 -0.776111 0.911144 0.158836
## (NM) 440: f = 27033.6 at 0.180901 -1.32601 -1.49181 -1.7089 0.407427 0.211097 -0.462609 -0.22093 -0.758758 0.133505 0.783215 0.0749241 -0.181821 -0.145672 -0.203573 0.333202 -0.0226902 0.0986628 0.474219 -0.776181 0.911128 0.158779
## (NM) 460: f = 27033.6 at 0.180859 -1.32566 -1.49195 -1.70924 0.407258 0.211204 -0.462595 -0.220915 -0.758472 0.133407 0.783244 0.0749406 -0.181816 -0.14565 -0.203675 0.333147 -0.0227284 0.0986546 0.474307 -0.776098 0.911122 0.158852
## (NM) 480: f = 27033.6 at 0.180835 -1.32633 -1.49194 -1.70916 0.407623 0.211003 -0.462559 -0.220887 -0.758799 0.13352 0.783273 0.0747903 -0.18176 -0.145673 -0.203634 0.333275 -0.0227903 0.0986454 0.474116 -0.77631 0.911096 0.158639
## (NM) 500: f = 27033.6 at 0.180895 -1.32617 -1.49185 -1.70929 0.407298 0.211262 -0.462622 -0.220924 -0.758655 0.133367 0.783248 0.0749432 -0.18182 -0.145673 -0.20363 0.333158 -0.0227349 0.0985906 0.474289 -0.776131 0.911157 0.158847
## (NM) 520: f = 27033.6 at 0.180869 -1.32602 -1.49193 -1.70921 0.40745 0.21122 -0.462573 -0.22091 -0.758705 0.133475 0.78324 0.0748788 -0.181793 -0.145717 -0.203621 0.33321 -0.022794 0.0986262 0.474197 -0.776246 0.911107 0.158714
## (NM) 540: f = 27033.6 at 0.180875 -1.32631 -1.49198 -1.70925 0.407417 0.211185 -0.462565 -0.220893 -0.758545 0.133501 0.783263 0.0748782 -0.181786 -0.145711 -0.203616 0.333223 -0.0227927 0.0986402 0.474181 -0.77625 0.911111 0.158721
## (NM) 560: f = 27033.6 at 0.180871 -1.32633 -1.49194 -1.70928 0.407434 0.211192 -0.462556 -0.220901 -0.758694 0.13349 0.783247 0.0748468 -0.181783 -0.145713 -0.203597 0.333222 -0.0227917 0.0986677 0.474203 -0.776276 0.911145 0.158706
## (NM) 580: f = 27033.6 at 0.180871 -1.32633 -1.49194 -1.70928 0.407434 0.211192 -0.462556 -0.220901 -0.758694 0.13349 0.783247 0.0748468 -0.181783 -0.145713 -0.203597 0.333222 -0.0227917 0.0986677 0.474203 -0.776276 0.911145 0.158706
## (NM) 600: f = 27033.6 at 0.180866 -1.32631 -1.49194 -1.70927 0.407452 0.211097 -0.462575 -0.220915 -0.758598 0.133603 0.783267 0.0749029 -0.181778 -0.145702 -0.203616 0.333195 -0.0227817 0.0986648 0.474204 -0.776231 0.911168 0.158721
## (NM) 620: f = 27033.6 at 0.18084 -1.32643 -1.49199 -1.70928 0.407442 0.211122 -0.462578 -0.220903 -0.75854 0.133457 0.783263 0.0748784 -0.181782 -0.145714 -0.203612 0.333237 -0.0228026 0.0986803 0.474204 -0.776285 0.911174 0.158687
## (NM) 640: f = 27033.6 at 0.180871 -1.32646 -1.49196 -1.7093 0.407473 0.211129 -0.462583 -0.220915 -0.758621 0.133508 0.783263 0.0749113 -0.181789 -0.145685 -0.203608 0.333203 -0.0227742 0.098659 0.474233 -0.776236 0.9112 0.158739
## (NM) 660: f = 27033.6 at 0.180884 -1.32634 -1.492 -1.70928 0.407467 0.211068 -0.462584 -0.220919 -0.758594 0.133594 0.783265 0.0749043 -0.181785 -0.145682 -0.203602 0.333203 -0.0227844 0.0986575 0.474231 -0.77625 0.911201 0.158726
## (NM) 680: f = 27033.6 at 0.180856 -1.3265 -1.49199 -1.70934 0.407518 0.211071 -0.462573 -0.220919 -0.758537 0.133588 0.783256 0.0748909 -0.181779 -0.145674 -0.203589 0.333221 -0.0228038 0.0986664 0.474205 -0.776288 0.911208 0.158694
## (NM) 700: f = 27033.6 at 0.180864 -1.3263 -1.49204 -1.70929 0.407561 0.211027 -0.46257 -0.220921 -0.758589 0.133689 0.783251 0.0748777 -0.181781 -0.145685 -0.203592 0.333236 -0.0228277 0.0986743 0.474192 -0.776307 0.911221 0.158659
## (NM) 720: f = 27033.6 at 0.180889 -1.32622 -1.49205 -1.70928 0.407533 0.211038 -0.462589 -0.220933 -0.758646 0.133669 0.783247 0.0748961 -0.181803 -0.145679 -0.203607 0.333237 -0.0228135 0.0986753 0.47425 -0.776267 0.911258 0.158709
## (NM) 740: f = 27033.6 at 0.180873 -1.32627 -1.49208 -1.70933 0.40761 0.211004 -0.462572 -0.220933 -0.758629 0.133713 0.783233 0.0748672 -0.1818 -0.145672 -0.203591 0.333266 -0.0228455 0.0986874 0.474217 -0.776329 0.911273 0.158665
## (NM) 760: f = 27033.6 at 0.180899 -1.3262 -1.49208 -1.70931 0.407644 0.210968 -0.462561 -0.220921 -0.758694 0.133756 0.783236 0.0748462 -0.181798 -0.14568 -0.203595 0.333278 -0.0228544 0.0986901 0.474206 -0.776358 0.911266 0.158666
## (NM) 780: f = 27033.6 at 0.180882 -1.32627 -1.4921 -1.70931 0.407649 0.210964 -0.462579 -0.220935 -0.758674 0.13375 0.783238 0.0748571 -0.181811 -0.145692 -0.203602 0.333289 -0.0228484 0.0987005 0.474241 -0.776368 0.911314 0.158698
## (NM) 800: f = 27033.6 at 0.180881 -1.32625 -1.4921 -1.70931 0.407646 0.210966 -0.46258 -0.220935 -0.758679 0.133749 0.783239 0.074858 -0.181811 -0.145692 -0.203603 0.333288 -0.0228481 0.098699 0.474242 -0.776366 0.911314 0.158699
## (NM) 820: f = 27033.6 at 0.180878 -1.32627 -1.49208 -1.70933 0.407651 0.210956 -0.462564 -0.220925 -0.758675 0.133742 0.783235 0.074841 -0.181798 -0.145682 -0.203586 0.333289 -0.0228445 0.0986987 0.474204 -0.776387 0.911277 0.158662
## (NM) 840: f = 27033.6 at 0.180886 -1.32623 -1.49206 -1.70932 0.407634 0.211008 -0.462565 -0.220927 -0.758741 0.133731 0.78324 0.0748481 -0.181799 -0.145691 -0.203599 0.333277 -0.0228345 0.0987028 0.474219 -0.776352 0.911278 0.158677
## (NM) 860: f = 27033.6 at 0.18088 -1.32622 -1.49209 -1.70935 0.407648 0.210977 -0.462577 -0.220937 -0.758719 0.133748 0.783226 0.0748411 -0.181812 -0.145681 -0.203593 0.333309 -0.022851 0.0987179 0.47424 -0.776379 0.911317 0.158679
## (NM) 880: f = 27033.6 at 0.180874 -1.32623 -1.49205 -1.70935 0.407651 0.211007 -0.46256 -0.220924 -0.758764 0.133727 0.783224 0.0748229 -0.181801 -0.145688 -0.203593 0.333312 -0.0228422 0.0987187 0.474208 -0.776381 0.911272 0.158658
## (NM) 900: f = 27033.6 at 0.180869 -1.32625 -1.49206 -1.70932 0.407631 0.211015 -0.462573 -0.220932 -0.758752 0.133664 0.783235 0.074834 -0.181809 -0.145696 -0.203604 0.3333 -0.02284 0.098714 0.474236 -0.776377 0.911294 0.158666
## (NM) 920: f = 27033.6 at 0.180871 -1.32622 -1.49204 -1.70934 0.407662 0.211034 -0.462566 -0.220927 -0.758797 0.1337 0.783231 0.0748282 -0.181808 -0.145703 -0.203605 0.333297 -0.0228368 0.0987071 0.474226 -0.776377 0.911287 0.158681
## (NM) 940: f = 27033.6 at 0.180879 -1.32623 -1.49204 -1.70937 0.40767 0.211039 -0.462567 -0.22093 -0.758784 0.133707 0.783229 0.0748206 -0.18181 -0.145695 -0.203596 0.333314 -0.0228366 0.0987173 0.474232 -0.776385 0.911295 0.158678
## (NM) 960: f = 27033.6 at 0.180886 -1.32617 -1.49203 -1.70934 0.407669 0.211024 -0.46257 -0.220936 -0.758818 0.133714 0.783226 0.0748261 -0.181813 -0.145692 -0.203596 0.333297 -0.0228295 0.0987159 0.474238 -0.776373 0.911295 0.158675
## (NM) 980: f = 27033.6 at 0.180886 -1.32616 -1.49202 -1.70935 0.407676 0.21103 -0.462569 -0.220937 -0.758829 0.133708 0.783226 0.0748218 -0.181814 -0.145692 -0.203595 0.333299 -0.0228286 0.0987175 0.474238 -0.776376 0.911295 0.158674
## (NM) 1000: f = 27033.6 at 0.180873 -1.32619 -1.49201 -1.70937 0.407693 0.211065 -0.462566 -0.220935 -0.758844 0.133651 0.783227 0.0748046 -0.181814 -0.145697 -0.20359 0.333312 -0.0228238 0.0987228 0.474232 -0.7764 0.911293 0.158658
## (NM) 1020: f = 27033.6 at 0.180878 -1.32621 -1.492 -1.70937 0.407719 0.211081 -0.462562 -0.220933 -0.758891 0.133658 0.783228 0.074796 -0.181817 -0.145707 -0.203591 0.333321 -0.022821 0.0987313 0.47423 -0.776406 0.91129 0.158655
## (NM) 1040: f = 27033.6 at 0.180873 -1.32621 -1.49201 -1.70936 0.407713 0.211066 -0.462568 -0.22093 -0.758885 0.13367 0.78323 0.0748125 -0.181818 -0.145717 -0.203604 0.333315 -0.0228141 0.0987198 0.474228 -0.776401 0.911276 0.158661
## (NM) 1060: f = 27033.6 at 0.180874 -1.32623 -1.49201 -1.70937 0.407749 0.211067 -0.462566 -0.220935 -0.758913 0.133672 0.783227 0.0747944 -0.181824 -0.145718 -0.203597 0.333339 -0.0228206 0.0987393 0.474231 -0.776433 0.911295 0.158651
## (NM) 1080: f = 27033.6 at 0.18087 -1.32623 -1.492 -1.70939 0.407736 0.211089 -0.462569 -0.220937 -0.758907 0.133644 0.783228 0.0748008 -0.181825 -0.14572 -0.203601 0.333337 -0.02281 0.0987412 0.474237 -0.776425 0.911292 0.158656
## (NM) 1100: f = 27033.6 at 0.180876 -1.32624 -1.49199 -1.70938 0.40773 0.211101 -0.462573 -0.220939 -0.758929 0.133622 0.783233 0.0748061 -0.181829 -0.14573 -0.203605 0.333331 -0.0227898 0.0987422 0.47424 -0.776419 0.911274 0.158657
## (NM) 1120: f = 27033.6 at 0.18087 -1.32627 -1.492 -1.70938 0.407715 0.211096 -0.462574 -0.220934 -0.758873 0.133615 0.783233 0.0748157 -0.181826 -0.145724 -0.203598 0.333325 -0.0227904 0.0987307 0.47423 -0.776417 0.911264 0.158659
## (NM) 1140: f = 27033.6 at 0.180876 -1.32626 -1.49198 -1.7094 0.407698 0.211123 -0.462582 -0.220939 -0.758875 0.133576 0.783238 0.0748251 -0.181831 -0.14573 -0.203603 0.333326 -0.0227626 0.0987408 0.474242 -0.776403 0.91125 0.158663
## (NM) 1160: f = 27033.6 at 0.180884 -1.32628 -1.49199 -1.7094 0.407736 0.211109 -0.462571 -0.220934 -0.758898 0.133612 0.783236 0.0748093 -0.18183 -0.145731 -0.203593 0.33334 -0.0227666 0.098749 0.474222 -0.776428 0.911241 0.158647
## (NM) 1180: f = 27033.6 at 0.180882 -1.32627 -1.49201 -1.70941 0.407739 0.211094 -0.462577 -0.220936 -0.758878 0.133605 0.783235 0.0748142 -0.181832 -0.145728 -0.203594 0.333344 -0.0227672 0.0987505 0.474227 -0.776434 0.911249 0.15865
## (NM) 1200: f = 27033.6 at 0.180882 -1.32627 -1.49201 -1.70941 0.407739 0.211094 -0.462577 -0.220936 -0.758878 0.133605 0.783235 0.0748142 -0.181832 -0.145728 -0.203594 0.333344 -0.0227672 0.0987505 0.474227 -0.776434 0.911249 0.15865
## (NM) 1220: f = 27033.6 at 0.180884 -1.32627 -1.49201 -1.70942 0.407739 0.211091 -0.462576 -0.220934 -0.758882 0.133602 0.783236 0.0748136 -0.181832 -0.145729 -0.203594 0.333343 -0.0227647 0.0987512 0.474223 -0.776438 0.911243 0.158649
## (NM) 1240: f = 27033.6 at 0.180886 -1.32631 -1.49198 -1.70943 0.407742 0.211114 -0.462573 -0.220932 -0.758914 0.133566 0.783238 0.0748056 -0.181833 -0.145734 -0.203588 0.333342 -0.022747 0.0987542 0.474217 -0.776445 0.911224 0.158642
## (NM) 1260: f = 27033.6 at 0.18089 -1.32628 -1.49198 -1.70942 0.407742 0.211109 -0.46257 -0.22093 -0.758923 0.133573 0.783239 0.0748051 -0.181831 -0.145735 -0.203592 0.333339 -0.022749 0.0987561 0.474211 -0.776442 0.911217 0.158641
## (NM) 1280: f = 27033.6 at 0.180886 -1.32628 -1.49198 -1.70944 0.40775 0.211116 -0.462573 -0.220931 -0.758935 0.133568 0.783237 0.0748065 -0.181835 -0.14574 -0.203592 0.333345 -0.0227446 0.0987589 0.474213 -0.776447 0.911216 0.158643
## (NM) 1300: f = 27033.6 at 0.18089 -1.32629 -1.49199 -1.70943 0.407744 0.211101 -0.462573 -0.220932 -0.758926 0.133576 0.78324 0.074815 -0.181833 -0.145737 -0.20359 0.333335 -0.0227404 0.0987586 0.47421 -0.776441 0.911214 0.158642
## (NM) 1320: f = 27033.6 at 0.180884 -1.32627 -1.49199 -1.70942 0.407747 0.211097 -0.462571 -0.220931 -0.758927 0.133592 0.783238 0.0748145 -0.181831 -0.145736 -0.203592 0.333333 -0.0227535 0.0987524 0.474211 -0.77644 0.911225 0.158644
## (NM) 1340: f = 27033.6 at 0.180881 -1.32627 -1.49199 -1.70943 0.407747 0.2111 -0.462572 -0.220931 -0.758932 0.133577 0.783239 0.0748147 -0.181832 -0.145739 -0.203593 0.333335 -0.0227507 0.0987563 0.474212 -0.776443 0.911224 0.158643
## (NM) 1360: f = 27033.6 at 0.180884 -1.32626 -1.49198 -1.70943 0.407753 0.21111 -0.462572 -0.220933 -0.758932 0.133561 0.783237 0.0748152 -0.181833 -0.145735 -0.203583 0.333334 -0.0227397 0.0987613 0.474208 -0.776442 0.911215 0.158637
## (NM) 1380: f = 27033.6 at 0.180881 -1.32625 -1.49198 -1.70943 0.407753 0.211103 -0.462571 -0.220931 -0.758935 0.133578 0.783237 0.0748181 -0.181832 -0.145737 -0.203585 0.333331 -0.0227449 0.0987585 0.474207 -0.776439 0.911218 0.15864
## (NM) 1400: f = 27033.6 at 0.18088 -1.32624 -1.49197 -1.70944 0.407758 0.211126 -0.462572 -0.220935 -0.758942 0.133553 0.783235 0.0748227 -0.181833 -0.145736 -0.203577 0.333328 -0.022732 0.0987672 0.474208 -0.77643 0.911212 0.158637
## (NM) 1420: f = 27033.6 at 0.180876 -1.32621 -1.49197 -1.70943 0.40776 0.21112 -0.462571 -0.220934 -0.758964 0.133553 0.783235 0.0748175 -0.181834 -0.14574 -0.203582 0.33333 -0.0227379 0.098767 0.474208 -0.776438 0.911218 0.158637
## (NM) 1440: f = 27033.6 at 0.180875 -1.32623 -1.49197 -1.70943 0.407748 0.211126 -0.462573 -0.220934 -0.758945 0.133548 0.783237 0.0748264 -0.181832 -0.145741 -0.203583 0.333324 -0.0227338 0.0987644 0.474208 -0.776426 0.91121 0.158639
## (NM) 1460: f = 27033.6 at 0.180874 -1.32622 -1.49197 -1.70943 0.407755 0.211121 -0.46257 -0.220933 -0.758956 0.133545 0.783236 0.0748238 -0.181831 -0.14574 -0.20358 0.333322 -0.0227355 0.0987657 0.474203 -0.776429 0.91121 0.158634
## (NM) 1480: f = 27033.6 at 0.180874 -1.32622 -1.49197 -1.70943 0.407744 0.211123 -0.462573 -0.220934 -0.75895 0.133536 0.783238 0.0748259 -0.181833 -0.145743 -0.203585 0.333323 -0.022734 0.0987637 0.474209 -0.776428 0.911209 0.158637
## (NM) 1500: f = 27033.6 at 0.180876 -1.32624 -1.49197 -1.70943 0.407745 0.211129 -0.462573 -0.220933 -0.758942 0.133537 0.783238 0.0748262 -0.181831 -0.145741 -0.203582 0.333323 -0.0227327 0.0987642 0.474208 -0.776423 0.911206 0.158635
## (NM) 1520: f = 27033.6 at 0.180873 -1.32623 -1.49197 -1.70944 0.407757 0.21113 -0.462572 -0.220934 -0.758964 0.133528 0.783237 0.0748222 -0.181833 -0.145744 -0.20358 0.333328 -0.0227268 0.0987703 0.474205 -0.776433 0.911203 0.15863
## (NM) 1540: f = 27033.6 at 0.180873 -1.32623 -1.49197 -1.70944 0.407753 0.211139 -0.462571 -0.220932 -0.758965 0.133509 0.783236 0.0748207 -0.181833 -0.145746 -0.203577 0.333329 -0.0227237 0.0987731 0.474203 -0.776432 0.911195 0.158627
## (NM) 1560: f = 27033.6 at 0.180872 -1.32624 -1.49197 -1.70944 0.407753 0.211137 -0.46257 -0.220931 -0.758965 0.133509 0.783238 0.0748202 -0.181832 -0.145748 -0.203579 0.33333 -0.0227263 0.0987747 0.474202 -0.776433 0.911196 0.158624
## (NM) 1580: f = 27033.6 at 0.180874 -1.32626 -1.49198 -1.70944 0.407758 0.21113 -0.46257 -0.22093 -0.758965 0.133515 0.783239 0.0748209 -0.181832 -0.14575 -0.20358 0.333332 -0.0227231 0.098775 0.4742 -0.776438 0.911193 0.158622
## (NM) 1600: f = 27033.6 at 0.180873 -1.32623 -1.49197 -1.70944 0.407758 0.211139 -0.46257 -0.22093 -0.758979 0.133498 0.783238 0.0748203 -0.181832 -0.145752 -0.203579 0.333328 -0.0227181 0.0987765 0.474198 -0.776433 0.911185 0.15862
## (NM) 1620: f = 27033.6 at 0.180872 -1.32625 -1.49198 -1.70944 0.40776 0.211138 -0.462568 -0.22093 -0.758978 0.133497 0.783239 0.0748195 -0.181831 -0.145753 -0.203578 0.33333 -0.0227195 0.0987798 0.474198 -0.776433 0.911185 0.158615
## (NM) 1640: f = 27033.6 at 0.180873 -1.32623 -1.49198 -1.70944 0.407755 0.211129 -0.462573 -0.220931 -0.758955 0.133517 0.783236 0.074823 -0.181831 -0.145745 -0.203577 0.333327 -0.0227258 0.0987719 0.474202 -0.776426 0.911195 0.158625
## (NM) 1660: f = 27033.6 at 0.180876 -1.32624 -1.49198 -1.70944 0.407764 0.211134 -0.46257 -0.22093 -0.758977 0.133505 0.783237 0.0748167 -0.181831 -0.145752 -0.203578 0.333332 -0.022719 0.0987787 0.474199 -0.776434 0.911185 0.158617
## (NM) 1680: f = 27033.6 at 0.180877 -1.32625 -1.49199 -1.70944 0.407769 0.211128 -0.46257 -0.22093 -0.758972 0.133516 0.783238 0.0748193 -0.181829 -0.145754 -0.203576 0.333331 -0.02272 0.0987807 0.474199 -0.776427 0.911185 0.158615
## (NM) 1700: f = 27033.6 at 0.180877 -1.32622 -1.49199 -1.70945 0.407801 0.211132 -0.462569 -0.220932 -0.759018 0.133513 0.783235 0.0748139 -0.181829 -0.14576 -0.203574 0.333332 -0.0227143 0.0987893 0.474196 -0.776427 0.911183 0.158608
## (NM) 1720: f = 27033.6 at 0.180881 -1.32622 -1.492 -1.70945 0.407797 0.211123 -0.462572 -0.220931 -0.758992 0.13352 0.783233 0.0748175 -0.181829 -0.145756 -0.203569 0.333332 -0.0227138 0.0987873 0.474199 -0.776419 0.911181 0.15861
## (NM) 1740: f = 27033.6 at 0.180881 -1.32621 -1.49199 -1.70945 0.407798 0.211125 -0.462571 -0.220932 -0.758995 0.133534 0.783233 0.0748163 -0.181829 -0.145756 -0.203573 0.333332 -0.0227179 0.0987835 0.4742 -0.77642 0.911187 0.158616
## (NM) 1760: f = 27033.6 at 0.180877 -1.32621 -1.49199 -1.70946 0.40781 0.211133 -0.462571 -0.220931 -0.759015 0.133514 0.783232 0.0748161 -0.18183 -0.145763 -0.203568 0.333335 -0.0227107 0.0987927 0.474197 -0.776422 0.911179 0.158608
## (NM) 1780: f = 27033.6 at 0.180877 -1.32621 -1.49199 -1.70946 0.40781 0.211133 -0.462571 -0.220931 -0.759015 0.133514 0.783232 0.0748161 -0.18183 -0.145763 -0.203568 0.333335 -0.0227107 0.0987927 0.474197 -0.776422 0.911179 0.158608
## (NM) 1800: f = 27033.6 at 0.180877 -1.32621 -1.49199 -1.70946 0.40781 0.211133 -0.462571 -0.220931 -0.759015 0.133514 0.783232 0.0748161 -0.18183 -0.145763 -0.203568 0.333335 -0.0227107 0.0987927 0.474197 -0.776422 0.911179 0.158608
## (NM) 1820: f = 27033.6 at 0.180878 -1.32621 -1.49199 -1.70946 0.407813 0.211141 -0.462572 -0.220933 -0.759016 0.133517 0.783231 0.0748106 -0.181829 -0.14576 -0.203569 0.333338 -0.0227153 0.0987915 0.474202 -0.776419 0.911187 0.15861
## (NM) 1840: f = 27033.6 at 0.180878 -1.32621 -1.49199 -1.70946 0.407815 0.211141 -0.462572 -0.220933 -0.759017 0.133517 0.78323 0.0748105 -0.181829 -0.145761 -0.203569 0.333338 -0.0227149 0.098792 0.474203 -0.776418 0.911187 0.15861
## (NM) 1860: f = 27033.6 at 0.180877 -1.32622 -1.49199 -1.70946 0.40782 0.211147 -0.462571 -0.220932 -0.759028 0.133506 0.783232 0.0748102 -0.181829 -0.145766 -0.203568 0.333339 -0.0227089 0.0987952 0.4742 -0.776419 0.911179 0.158606
## (NM) 1880: f = 27033.6 at 0.180876 -1.32621 -1.49199 -1.70947 0.407824 0.211148 -0.462571 -0.220932 -0.759029 0.133505 0.783231 0.0748104 -0.18183 -0.145767 -0.203567 0.333341 -0.0227072 0.0987965 0.4742 -0.776423 0.911179 0.158607
## (NM) 1900: f = 27033.6 at 0.180876 -1.32623 -1.49199 -1.70947 0.407818 0.211148 -0.462572 -0.220931 -0.759022 0.133498 0.783233 0.0748135 -0.181831 -0.145768 -0.203567 0.33334 -0.0227033 0.0987952 0.474199 -0.776422 0.911174 0.158607
## (NM) 1920: f = 27033.6 at 0.180874 -1.32622 -1.49198 -1.70947 0.407816 0.211155 -0.462572 -0.22093 -0.759024 0.133491 0.783231 0.0748075 -0.181831 -0.145767 -0.203568 0.333342 -0.0227072 0.0987924 0.4742 -0.776424 0.911176 0.15861summary(m3)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## cbind(Nb_adults, Nb_eggs) ~ -1 + Generation * Fruit_s * Treatmentrel +
## (1 | Line)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 27077.6 27196.9 -13516.8 27033.6 1652
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.2174 -2.2315 0.0267 2.2418 17.5345
##
## Random effects:
## Groups Name Variance Std.Dev.
## Line (Intercept) 0.03272 0.1809
## Number of obs: 1674, groups: Line, 26
##
## Fixed effects:
## Estimate Std. Error
## Generation0 -1.32622 0.18209
## Generation29 -1.49198 0.10661
## Generation7 -1.70947 0.09881
## Fruit_sCranberry 0.40782 0.13255
## Fruit_sStrawberry 0.21116 0.13061
## TreatmentrelCherry -0.46257 0.03015
## TreatmentrelCranberry -0.22093 0.03010
## Generation29:Fruit_sCranberry -0.75902 0.18939
## Generation29:Fruit_sStrawberry 0.13349 0.19924
## Generation29:TreatmentrelCherry 0.78323 0.04268
## Generation7:TreatmentrelCherry 0.07481 0.06104
## Generation29:TreatmentrelCranberry -0.18183 0.04520
## Generation7:TreatmentrelCranberry -0.14577 0.06348
## Fruit_sCranberry:TreatmentrelCherry -0.20357 0.07256
## Fruit_sStrawberry:TreatmentrelCherry 0.33334 0.06141
## Fruit_sCranberry:TreatmentrelCranberry -0.02271 0.07132
## Fruit_sStrawberry:TreatmentrelCranberry 0.09879 0.06456
## Generation29:Fruit_sCranberry:TreatmentrelCherry 0.47420 0.08272
## Generation29:Fruit_sStrawberry:TreatmentrelCherry -0.77642 0.07473
## Generation29:Fruit_sCranberry:TreatmentrelCranberry 0.91118 0.08292
## Generation29:Fruit_sStrawberry:TreatmentrelCranberry 0.15861 0.07833
## z value Pr(>|z|)
## Generation0 -7.283 3.26e-13 ***
## Generation29 -13.994 < 2e-16 ***
## Generation7 -17.300 < 2e-16 ***
## Fruit_sCranberry 3.077 0.00209 **
## Fruit_sStrawberry 1.617 0.10594
## TreatmentrelCherry -15.342 < 2e-16 ***
## TreatmentrelCranberry -7.341 2.13e-13 ***
## Generation29:Fruit_sCranberry -4.008 6.13e-05 ***
## Generation29:Fruit_sStrawberry 0.670 0.50285
## Generation29:TreatmentrelCherry 18.352 < 2e-16 ***
## Generation7:TreatmentrelCherry 1.226 0.22035
## Generation29:TreatmentrelCranberry -4.023 5.75e-05 ***
## Generation7:TreatmentrelCranberry -2.296 0.02165 *
## Fruit_sCranberry:TreatmentrelCherry -2.806 0.00502 **
## Fruit_sStrawberry:TreatmentrelCherry 5.428 5.69e-08 ***
## Fruit_sCranberry:TreatmentrelCranberry -0.318 0.75018
## Fruit_sStrawberry:TreatmentrelCranberry 1.530 0.12594
## Generation29:Fruit_sCranberry:TreatmentrelCherry 5.733 9.89e-09 ***
## Generation29:Fruit_sStrawberry:TreatmentrelCherry -10.390 < 2e-16 ***
## Generation29:Fruit_sCranberry:TreatmentrelCranberry 10.988 < 2e-16 ***
## Generation29:Fruit_sStrawberry:TreatmentrelCranberry 2.025 0.04287 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 15 columns / coefficients4 CORRELATED RESPONSES
4.1 Analysis MA and correlation
# ##### Create empty dataframe with slopes and confidence interval for all pairwise
Estimates_pairwise <- data.frame(Variables = rep(rep(c("correlation","cor_CI_inf","cor_CI_sup",
"slope", "intercept"),2),3),
Generation = rep(c(rep(7, 5), rep(29, 5)),3),
Pairwise = rep(c("Cherry_Cranberry",
"Cranberry_Strawberry",
"Strawberry_Cherry"), each=10),
Estimates = NA)
#########################
######################### G7
#########################
#######
####### Cherry Cranberry
#######
## Compute weighted correlation
weightedcor <- sjstats:::weighted_correlation(TEMP_dataG7_CheCran, x=logchange_symp, y=logchange_allop, weights=N_sumsympallop, ci.lvl = 0.95)
## With cor.tes, the estimation of correlation coefficient is right, but the confidence interval is wrong
cor.test(x=TEMP_dataG7_CheCran[rep(1:nrow(TEMP_dataG7_CheCran), TEMP_dataG7_CheCran$N_sumsympallop), "logchange_symp"], y=TEMP_dataG7_CheCran[rep(1:nrow(TEMP_dataG7_CheCran), TEMP_dataG7_CheCran$N_sumsympallop), "logchange_allop"])##
## Pearson's product-moment correlation
##
## data: TEMP_dataG7_CheCran[rep(1:nrow(TEMP_dataG7_CheCran), TEMP_dataG7_CheCran$N_sumsympallop), "logchange_symp"] and TEMP_dataG7_CheCran[rep(1:nrow(TEMP_dataG7_CheCran), TEMP_dataG7_CheCran$N_sumsympallop), "logchange_allop"]
## t = 6.3642, df = 136, p-value = 2.79e-09
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3390707 0.5982491
## sample estimates:
## cor
## 0.4790334## Compute Major Axis regression
model <- lmodel2::lmodel2(logchange_allop ~ logchange_symp,
range.y = "interval",range.x = "interval",
data = TEMP_dataG7_CheCran[rep(1:nrow(TEMP_dataG7_CheCran), TEMP_dataG7_CheCran$N_sumsympallop),], nperm=0)
# Fill in data
Estimates_pairwise <- fillout_estimate_correlation (Estimates_pairwise, 7, "Cherry_Cranberry")
#######
####### Cranberry Strawberry
#######
## Compute weighted correlation
weightedcor <- sjstats:::weighted_correlation(TEMP_dataG7_CranStraw, x=logchange_symp, y=logchange_allop, weights=N_sumsympallop, ci.lvl = 0.95)
## Compute Major Axis regression
model <- lmodel2::lmodel2(logchange_allop ~ logchange_symp,
range.y = "interval",range.x = "interval",
data = TEMP_dataG7_CranStraw[rep(1:nrow(TEMP_dataG7_CranStraw), TEMP_dataG7_CranStraw$N_sumsympallop),], nperm=0)
# Fill in data
Estimates_pairwise <- fillout_estimate_correlation (Estimates_pairwise, 7, "Cranberry_Strawberry")
#######
####### Strawberry Cherry
#######
## Compute weighted correlation
weightedcor <- sjstats:::weighted_correlation(TEMP_dataG7_StrawChe, x=logchange_symp, y=logchange_allop, weights=N_sumsympallop, ci.lvl = 0.95)
## Compute Major Axis regression
model <- lmodel2::lmodel2(logchange_allop ~ logchange_symp,
range.y = "interval",range.x = "interval",
data = TEMP_dataG7_StrawChe[rep(1:nrow(TEMP_dataG7_StrawChe), TEMP_dataG7_StrawChe$N_sumsympallop),], nperm=0)
# Fill in data
Estimates_pairwise <- fillout_estimate_correlation (Estimates_pairwise, 7, "Strawberry_Cherry")
#########################
######################### G29
#########################
#######
####### Cherry Cranberry
#######
## Compute unweighted correlation
unweightedcor <- cor.test(x=TEMP_dataG29_CheCran$logchange_symp, y=TEMP_dataG29_CheCran$logchange_allop)
## Compute weighted correlation
weightedcor <- sjstats:::weighted_correlation(TEMP_dataG29_CheCran, x=logchange_symp, y=logchange_allop, weights=N_sumsympallop, ci.lvl = 0.95)
## Compute Major Axis regression
model <- lmodel2::lmodel2(logchange_allop ~ logchange_symp,
range.y = "interval",range.x = "interval",
data = TEMP_dataG29_CheCran[rep(1:nrow(TEMP_dataG29_CheCran), TEMP_dataG29_CheCran$N_sumsympallop),], nperm=0)
# Fill in data
Estimates_pairwise <- fillout_estimate_correlation (Estimates_pairwise, 29, "Cherry_Cranberry")
#######
####### Cranberry Strawberry
#######
## Compute unweighted correlation
unweightedcor <- cor.test(x=TEMP_dataG29_CranStraw$logchange_symp, y=TEMP_dataG29_CranStraw$logchange_allop)
## Compute weighted correlation
weightedcor <- sjstats:::weighted_correlation(TEMP_dataG29_CranStraw, x=logchange_symp, y=logchange_allop, weights=N_sumsympallop, ci.lvl = 0.95)
## Compute Major Axis regression
model <- lmodel2::lmodel2(logchange_allop ~ logchange_symp,
range.y = "interval",range.x = "interval",
data = TEMP_dataG29_CranStraw[rep(1:nrow(TEMP_dataG29_CranStraw), TEMP_dataG29_CranStraw$N_sumsympallop),], nperm=0)
# Fill in data
Estimates_pairwise <- fillout_estimate_correlation (Estimates_pairwise, 29, "Cranberry_Strawberry")
#######
####### Strawberry Cherry
#######
## Compute unweighted correlation
unweightedcor <- cor.test(x=TEMP_dataG29_StrawChe$logchange_symp, y=TEMP_dataG29_StrawChe$logchange_allop)
## Compute weighted correlation
weightedcor <- sjstats:::weighted_correlation(TEMP_dataG29_StrawChe, x=logchange_symp, y=logchange_allop, weights=N_sumsympallop, ci.lvl = 0.95)
## Compute Major Axis regression
model <- lmodel2::lmodel2(logchange_allop ~ logchange_symp,
range.y = "interval",range.x = "interval",
data = TEMP_dataG29_StrawChe[rep(1:nrow(TEMP_dataG29_StrawChe), TEMP_dataG29_StrawChe$N_sumsympallop),], nperm=0)
# Fill in data
Estimates_pairwise <- fillout_estimate_correlation (Estimates_pairwise, 29, "Strawberry_Cherry")
## Some estimates are outside [-1, 1]
Estimates_pairwise[Estimates_pairwise$Variables=="cor_CI_inf",]## Variables Generation Pairwise Estimates
## 2 cor_CI_inf 7 Cherry_Cranberry 0.001431407
## 7 cor_CI_inf 29 Cherry_Cranberry -1.051419194
## 12 cor_CI_inf 7 Cranberry_Strawberry 0.105487696
## 17 cor_CI_inf 29 Cranberry_Strawberry 0.120501768
## 22 cor_CI_inf 7 Strawberry_Cherry 0.188354905
## 27 cor_CI_inf 29 Strawberry_Cherry 0.145604434Estimates_pairwise[Estimates_pairwise$Variables=="cor_CI_sup",]## Variables Generation Pairwise Estimates
## 3 cor_CI_sup 7 Cherry_Cranberry 0.95663530
## 8 cor_CI_sup 29 Cherry_Cranberry -0.08369498
## 13 cor_CI_sup 7 Cranberry_Strawberry 0.96523247
## 18 cor_CI_sup 29 Cranberry_Strawberry 1.06648658
## 23 cor_CI_sup 7 Strawberry_Cherry 1.04499320
## 28 cor_CI_sup 29 Strawberry_Cherry 1.20639674## Change the estimate to the boundary
Estimates_pairwise$Estimates[Estimates_pairwise$Variables=="cor_CI_inf"&Estimates_pairwise$Estimates<(-1)] <- rep(-1, sum(Estimates_pairwise$Variables=="cor_CI_inf"&Estimates_pairwise$Estimates<(-1)))
Estimates_pairwise$Estimates[Estimates_pairwise$Variables=="cor_CI_sup"&Estimates_pairwise$Estimates > 1] <- rep(1, sum(Estimates_pairwise$Variables=="cor_CI_sup"&Estimates_pairwise$Estimates > 1))4.2 Compute confidence interval of the difference
computeCIcordifG7G29(pair="Cherry_Cranberry")## correlationdifference lowerCI higherCI
## 1.0465904 0.3667183 1.6908811computeCIcordifG7G29(pair="Cranberry_Strawberry")## correlationdifference lowerCI higherCI
## -0.05813409 -0.64977355 0.58101541computeCIcordifG7G29(pair="Strawberry_Cherry")## correlationdifference lowerCI higherCI
## -0.05932653 -0.59638604 0.595088364.3 Plot Fitness
ymin = -50
ymax = 50
##################################################
################## G7
# Limits
ymin_CheCranG7=min(min(TEMP_dataG7_CheCran$lowCIlogfitnesschange_allop, na.rm= TRUE),
min(TEMP_dataG7_CheCran$lowCIlogfitnesschange_symp, na.rm= TRUE))
ymax_CheCranG7=max(max(TEMP_dataG7_CheCran$upCIlogfitnesschange_allop, na.rm= TRUE),
max(TEMP_dataG7_CheCran$upCIlogfitnesschange_symp, na.rm= TRUE))
lim_text<-2
# Plot
CheCran_G7 <- ggplot(data = TEMP_dataG7_CheCran) +
geom_abline(intercept = Estimates_pairwise$Estimates[Estimates_pairwise$Generation == 7 &
Estimates_pairwise$Pairwise == "Cherry_Cranberry" &
Estimates_pairwise$Variables == "intercept"],
slope = Estimates_pairwise$Estimates[Estimates_pairwise$Generation == 7 &
Estimates_pairwise$Pairwise == "Cherry_Cranberry" &
Estimates_pairwise$Variables == "slope"],
colour = "black", size = 0.75) +
geom_text(x = 0, y = 2,
label = eq_r(gen = 7, pair = "Cherry_Cranberry"), parse = TRUE,
color="black", size = 3.5) +
geom_errorbar(aes(x = logchange_symp, ymin = lowCIlogfitnesschange_allop,
ymax = upCIlogfitnesschange_allop,
color = Fruit_s),
width= 0.02, size = 0.3, alpha = 0.8) +
geom_errorbarh(aes(y = logchange_allop, xmin = lowCIlogfitnesschange_symp,
xmax = upCIlogfitnesschange_symp,
color = Fruit_s),
height = 0.02, size = 0.3, alpha = 0.8) +
geom_point(aes(x = logchange_symp, y = logchange_allop,
color = Fruit_s,fill = Fruit_s, shape = Treatment),
size = 3, fill = "white", stroke =1.2) +
xlab("Fitness change in\nselective environment") +
ylab("Fitness change in\nalternative environment") +
ggtitle("Cherry vs. Cranberry") +
theme_LO_sober +
labs(shape = "Test fruit", color = "Evolution fruit") +
scale_shape_manual(values = c(21, 22)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424")) +
coord_cartesian(ylim = c(ymin_CheCranG7, ymax_CheCranG7),
xlim = c(ymin_CheCranG7, ymax_CheCranG7))
CheCran_G7
# Limits
ymin_CranStrawG7=min(min(TEMP_dataG7_CranStraw$lowCIlogfitnesschange_allop, na.rm= TRUE),
min(TEMP_dataG7_CranStraw$lowCIlogfitnesschange_symp, na.rm= TRUE))
ymax_CranStrawG7=max(max(TEMP_dataG7_CranStraw$upCIlogfitnesschange_allop, na.rm= TRUE),
max(TEMP_dataG7_CranStraw$upCIlogfitnesschange_symp, na.rm= TRUE))
lim_text<-ymin_CranStrawG7+0.99*(ymax_CranStrawG7-ymin_CranStrawG7)
CranStraw_G7 <- ggplot(data = TEMP_dataG7_CranStraw) +
geom_abline(intercept = Estimates_pairwise$Estimates[Estimates_pairwise$Generation == 7 &
Estimates_pairwise$Pairwise == "Cranberry_Strawberry" &
Estimates_pairwise$Variables == "intercept"],
slope = Estimates_pairwise$Estimates[Estimates_pairwise$Generation == 7 &
Estimates_pairwise$Pairwise == "Cranberry_Strawberry" &
Estimates_pairwise$Variables == "slope"],
colour = "black", size = 0.75) +
geom_errorbar(aes(x = logchange_symp, ymin = lowCIlogfitnesschange_allop,
ymax = upCIlogfitnesschange_allop,
color = Fruit_s),
width= 0.02, size = 0.3, alpha = 0.8) +
geom_errorbarh(aes(y = logchange_allop, xmin = lowCIlogfitnesschange_symp,
xmax = upCIlogfitnesschange_allop,
color = Fruit_s),
height = 0.02, size = 0.3, alpha = 0.8) +
geom_point(aes(x =logchange_symp, y = logchange_allop, color = Fruit_s,
fill = Fruit_s, shape = Treatment),
size =3, fill = "white", stroke =1.2) +
geom_text(x = -0.25, y = lim_text, label = eq_r(gen = 7, pair = "Cranberry_Strawberry"), parse = TRUE, color="black", size = 3.5) +
xlab("Fitness change in\nselective environment") +
ylab("Fitness change in\nalternative environment") +
ggtitle("Cranberry vs. Strawberry") +
coord_cartesian(ylim = c(ymin_CranStrawG7, ymax_CranStrawG7),
xlim = c(ymin_CranStrawG7, ymax_CranStrawG7)) +
labs(shape = "Test fruit", color = "Selection fruit") +
scale_shape_manual(values = c(22, 24)) +
scale_color_manual(values = c("#FDB424", "#3FAA96")) +
theme_LO_sober
CranStraw_G7
# Limits
ymin_StrawCheG7=min(min(TEMP_dataG7_StrawChe$lowCIlogfitnesschange_allop, na.rm= TRUE),
min(TEMP_dataG7_StrawChe$lowCIlogfitnesschange_symp, na.rm= TRUE))
ymax_StrawCheG7=max(max(TEMP_dataG7_StrawChe$upCIlogfitnesschange_allop, na.rm= TRUE),
max(TEMP_dataG7_StrawChe$lowCIlogfitnesschange_symp, na.rm= TRUE))
lim_text<-ymin_StrawCheG7+0.99*(ymax_StrawCheG7-ymin_StrawCheG7)
StrawChe_G7 <- ggplot(data = TEMP_dataG7_StrawChe) +
geom_abline(intercept = Estimates_pairwise$Estimates[Estimates_pairwise$Generation == 7 &
Estimates_pairwise$Pairwise == "Strawberry_Cherry" &
Estimates_pairwise$Variables == "intercept"],
slope = Estimates_pairwise$Estimates[Estimates_pairwise$Generation == 7 &
Estimates_pairwise$Pairwise == "Strawberry_Cherry" &
Estimates_pairwise$Variables == "slope"],
colour = "black", size = 0.75) +
geom_errorbar(aes(x =logchange_symp,
ymin = lowCIlogfitnesschange_allop,
ymax = upCIlogfitnesschange_allop,
color = Fruit_s),
width= 0.02, size = 0.3, alpha = 0.8) +
geom_errorbarh(aes(y = logchange_allop,
xmin = lowCIlogfitnesschange_symp,
xmax = upCIlogfitnesschange_symp,
color = Fruit_s),
height = 0.02, size = 0.3, alpha = 0.8) +
geom_point(aes(x = logchange_symp, y = logchange_allop, color = Fruit_s,fill = Fruit_s, shape = Treatment),
size =3, fill = "white", stroke =1.2) +
geom_text(x = -0.13, y = lim_text, label = eq_r(gen = 7, pair = "Strawberry_Cherry"), parse = TRUE, color="black", size = 3.5) +
xlab("Fitness change in\nselective environment") +
ylab("Fitness change in\nalternative environment") +
ggtitle("Strawberry vs. Cherry") +
labs(shape = "Test fruit", color = "Selection fruit") +
scale_shape_manual(values = c(21, 24)) +
scale_color_manual(values = c("#BC3C6D", "#3FAA96")) +
coord_cartesian(ylim = c(ymin_StrawCheG7, ymax_StrawCheG7),
xlim = c(ymin_StrawCheG7, ymax_StrawCheG7)) +
theme_LO_sober
StrawChe_G7
#####################################################################################
################################## G29 ##################################
#####################################################################################
TEMP_dataG29_CheCran## Treatment Line Fruit_s Generation N_symp logchange_symp
## 5 Cherry CEA Cherry 29 30 0.14767000
## 6 Cherry CEB Cherry 29 30 0.25038029
## 7 Cherry CEC Cherry 29 30 0.35854062
## 38 Cranberry CRA Cranberry 29 30 -0.14885350
## 39 Cranberry CRB Cranberry 29 30 0.29719689
## 40 Cranberry CRC Cranberry 29 30 0.02703717
## 41 Cranberry CRD Cranberry 29 30 -0.15627744
## 42 Cranberry CRE Cranberry 29 30 0.72513486
## lowCIlogfitnesschange_symp upCIlogfitnesschange_symp N_allop logchange_allop
## 5 -0.12317382 0.4301216 30 -0.21774071
## 6 -0.01964191 0.5320919 30 -0.59200204
## 7 0.08929875 0.6395485 30 -0.20670829
## 38 -0.42196368 0.1356310 30 0.32537794
## 39 0.02797352 0.5781852 30 0.16929862
## 40 -0.24433581 0.3099615 30 0.08474123
## 41 -0.42946769 0.1282789 30 0.54736475
## 42 0.45833897 1.0039294 30 -0.10496044
## lowCIlogfitnesschange_allop upCIlogfitnesschange_allop N_sumsympallop
## 5 -0.49161649 0.06743017 60
## 6 -0.87105791 -0.30220654 60
## 7 -0.48045799 0.07834961 60
## 38 0.05590556 0.60659377 60
## 39 -0.10136525 0.45158825 60
## 40 -0.18664831 0.36768394 60
## 41 0.27930142 0.82730827 60
## 42 -0.37821315 0.17965592 60TEMP_dataG29_CranStraw## Treatment Line Fruit_s Generation N_symp logchange_symp
## 38 Cranberry CRA Cranberry 29 30 -0.14885350
## 39 Cranberry CRB Cranberry 29 30 0.29719689
## 40 Cranberry CRC Cranberry 29 30 0.02703717
## 41 Cranberry CRD Cranberry 29 30 -0.15627744
## 42 Cranberry CRE Cranberry 29 30 0.72513486
## 73 Strawberry FRA Strawberry 29 30 0.15709032
## 74 Strawberry FRB Strawberry 29 30 0.56023100
## 75 Strawberry FRC Strawberry 29 30 0.36640738
## lowCIlogfitnesschange_symp upCIlogfitnesschange_symp N_allop logchange_allop
## 38 -0.42196368 0.1356310 30 -0.29731422
## 39 0.02797352 0.5781852 30 -0.24539416
## 40 -0.24433581 0.3099615 30 -0.28950778
## 41 -0.42946769 0.1282789 30 -1.02555272
## 42 0.45833897 1.0039294 30 -0.16810249
## 73 -0.11224578 0.4381748 30 -0.25158762
## 74 0.29332571 0.8391204 30 0.04303751
## 75 0.09845305 0.6462450 30 0.52109773
## lowCIlogfitnesschange_allop upCIlogfitnesschange_allop N_sumsympallop
## 38 -0.5708280 -0.012475350 60
## 39 -0.5183319 0.038928334 60
## 40 -0.5629331 -0.004748193 60
## 41 -1.3110679 -0.730048996 60
## 42 -0.4402347 0.115497271 60
## 73 -0.5258587 0.033937386 60
## 74 -0.2281917 0.325832559 60
## 75 0.2532712 0.800824675 60TEMP_dataG29_StrawChe## Treatment Line Fruit_s Generation N_symp logchange_symp
## 5 Cherry CEA Cherry 29 30 0.1476700
## 6 Cherry CEB Cherry 29 30 0.2503803
## 7 Cherry CEC Cherry 29 30 0.3585406
## 73 Strawberry FRA Strawberry 29 30 0.1570903
## 74 Strawberry FRB Strawberry 29 30 0.5602310
## 75 Strawberry FRC Strawberry 29 30 0.3664074
## lowCIlogfitnesschange_symp upCIlogfitnesschange_symp N_allop logchange_allop
## 5 -0.12317382 0.4301216 30 0.01971359
## 6 -0.01964191 0.5320919 30 -0.15850680
## 7 0.08929875 0.6395485 30 0.21131464
## 73 -0.11224578 0.4381748 30 -0.50599095
## 74 0.29332571 0.8391204 30 0.19377147
## 75 0.09845305 0.6462450 30 0.32151693
## lowCIlogfitnesschange_allop upCIlogfitnesschange_allop N_sumsympallop
## 5 -0.25069640 0.3017653 60
## 6 -0.43054314 0.1250069 60
## 7 -0.05763581 0.4920514 60
## 73 -0.78451399 -0.2166643 60
## 74 -0.07669333 0.4758818 60
## 75 0.05201722 0.6027574 60# Limits
ymin_CheCranG29=min(min(TEMP_dataG29_CheCran$lowCIlogfitnesschange_allop, na.rm= TRUE),
min(TEMP_dataG29_CheCran$lowCIlogfitnesschange_symp, na.rm= TRUE))
ymax_CheCranG29=max(max(TEMP_dataG29_CheCran$upCIlogfitnesschange_allop, na.rm= TRUE),
max(TEMP_dataG29_CheCran$upCIlogfitnesschange_symp, na.rm= TRUE))
lim_text<-ymin_CheCranG29+0.99*(ymax_CheCranG29-ymin_CheCranG29)
CheCran_G29 <- ggplot(data = TEMP_dataG29_CheCran) +
geom_abline(intercept = Estimates_pairwise$Estimates[Estimates_pairwise$Generation ==29 &
Estimates_pairwise$Pairwise == "Cherry_Cranberry" &
Estimates_pairwise$Variables == "intercept"],
slope = Estimates_pairwise$Estimates[Estimates_pairwise$Generation ==29 &
Estimates_pairwise$Pairwise == "Cherry_Cranberry" &
Estimates_pairwise$Variables == "slope"],
colour = "black", size = 0.75) +
geom_errorbar(aes(x =logchange_symp, ymin = lowCIlogfitnesschange_allop,
ymax = upCIlogfitnesschange_allop,
color = Fruit_s),
width= 0.02, size = 0.3, alpha = 0.8) +
geom_errorbarh(aes(y = logchange_allop, xmin = lowCIlogfitnesschange_symp,
xmax = upCIlogfitnesschange_symp,
color = Fruit_s),
height = 0.02, size = 0.3, alpha = 0.8) +
geom_point(aes(x =logchange_symp, y = logchange_allop, color = Fruit_s, fill = Fruit_s, shape = Treatment),
size =3, fill = "white", stroke =1.2) +
geom_text(x = 0.33, y = lim_text, label = eq_r(gen = 29, pair = "Cherry_Cranberry"), parse = TRUE, color="black", size = 3.5) +
xlab("Fitness change in\nselective environment") +
ylab("Fitness change in\nalternative environment") +
ggtitle("Cherry vs. Cranberry") +
labs(shape = "Test fruit", color = "Evolution fruit") +
scale_shape_manual(values = c(16, 15)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424")) +
coord_cartesian(ylim = c(ymin_CheCranG29, ymax_CheCranG29),
xlim = c(ymin_CheCranG29, ymax_CheCranG29)) +
theme_LO_sober
CheCran_G29
# Limits
ymin_CranStrawG29=min(min(TEMP_dataG29_CranStraw$lowCIlogfitnesschange_allop, na.rm= TRUE),
min(TEMP_dataG29_CranStraw$lowCIlogfitnesschange_symp, na.rm= TRUE))
ymax_CranStrawG29=max(max(TEMP_dataG29_CranStraw$upCIlogfitnesschange_allop, na.rm= TRUE),
max(TEMP_dataG29_CranStraw$upCIlogfitnesschange_symp, na.rm= TRUE))
lim_text<-ymin_CranStrawG29+0.99*(ymax_CranStrawG29-ymin_CranStrawG29)
#Plot
CranStraw_G29 <- ggplot(data = TEMP_dataG29_CranStraw) +
geom_abline(intercept = Estimates_pairwise$Estimates[Estimates_pairwise$Generation ==29 &
Estimates_pairwise$Pairwise == "Cranberry_Strawberry" &
Estimates_pairwise$Variables == "intercept"],
slope = Estimates_pairwise$Estimates[Estimates_pairwise$Generation ==29 &
Estimates_pairwise$Pairwise == "Cranberry_Strawberry" &
Estimates_pairwise$Variables == "slope"],
colour = "black", size = 0.75) +
geom_errorbar(aes(x =logchange_symp, ymin = lowCIlogfitnesschange_allop,
ymax = upCIlogfitnesschange_allop,
color = Fruit_s),
width= 0.02, size = 0.3, alpha = 0.8) +
geom_errorbarh(aes(y = logchange_allop, xmin = lowCIlogfitnesschange_symp,
xmax = upCIlogfitnesschange_symp,
color = Fruit_s),
height = 0.02, size = 0.3, alpha = 0.8) +
geom_point(aes(x =logchange_symp, y = logchange_allop,
color = Fruit_s,fill = Fruit_s, shape = Treatment),
size =3, fill = "white", stroke =1.2) +
geom_text(x = -0.45, y = lim_text, label = eq_r(gen = 29, pair = "Cranberry_Strawberry"), parse = TRUE, color="black", size = 3.5) +
xlab("Fitness change in\nselective environment") +
ylab("Fitness change in\nalternative environment") +
ggtitle("Cranberry vs. Strawberry") +
coord_cartesian(ylim = c(ymin_CranStrawG29, ymax_CranStrawG29),
xlim = c(ymin_CranStrawG29, ymax_CranStrawG29)) +
labs(shape = "Test fruit", color = "Selection fruit") +
scale_shape_manual(values = c(15, 17)) +
scale_color_manual(values = c("#FDB424", "#3FAA96")) +
theme_LO_sober
CranStraw_G29
# Limits
ymin_StrawCheG29=min(min(TEMP_dataG29_StrawChe$lowCIlogfitnesschange_allop, na.rm= TRUE),
min(TEMP_dataG29_StrawChe$lowCIlogfitnesschange_symp, na.rm= TRUE))
ymax_StrawCheG29=max(max(TEMP_dataG29_StrawChe$upCIlogfitnesschange_allop, na.rm= TRUE),
max(TEMP_dataG29_StrawChe$upCIlogfitnesschange_symp, na.rm= TRUE))
lim_text<-ymin_StrawCheG29+0.99*(ymax_StrawCheG29-ymin_StrawCheG29)
StrawChe_G29 <- ggplot(data = TEMP_dataG29_StrawChe) +
geom_abline(intercept = Estimates_pairwise$Estimates[Estimates_pairwise$Generation ==29 &
Estimates_pairwise$Pairwise == "Strawberry_Cherry" &
Estimates_pairwise$Variables == "intercept"],
slope = Estimates_pairwise$Estimates[Estimates_pairwise$Generation ==29 &
Estimates_pairwise$Pairwise == "Strawberry_Cherry" &
Estimates_pairwise$Variables == "slope"],
colour = "black", size = 0.75) +
geom_errorbar(aes(x =logchange_symp, ymin = lowCIlogfitnesschange_allop,
ymax = upCIlogfitnesschange_allop,
color = Fruit_s),
width= 0.02, size = 0.2, alpha =1) +
geom_errorbarh(aes(y = logchange_allop, xmin = lowCIlogfitnesschange_symp,
xmax = upCIlogfitnesschange_symp,
color = Fruit_s),
height = 0.02, size = 0.2, alpha =1) +
geom_point(aes(x =logchange_symp, y = logchange_allop,
color = Fruit_s, fill = Fruit_s, shape = Treatment),
size =3, fill = "white", stroke =1.2) +
geom_text(x = -0.2, y = lim_text,
label = eq_r(gen = 29, pair = "Strawberry_Cherry"),
parse = TRUE, color="black", size = 3.5) +
coord_cartesian(ylim = c(ymin_StrawCheG29, ymax_StrawCheG29),
xlim = c(ymin_StrawCheG29, ymax_StrawCheG29)) +
xlab("Fitness change in\nselective environment") +
ylab("Fitness change in\nalternative environment") +
ggtitle("Strawberry vs. Cherry") +
labs(shape = "Test fruit", color = "Selection fruit") +
scale_shape_manual(values = c(16, 17)) +
scale_color_manual(values = c("#BC3C6D", "#3FAA96")) +
theme_LO_sober
StrawChe_G29
legend_tradevide <- ggplot(data = data_logchange[data_logchange$Generation == "29",],
aes(x =logchange, y = logchange, color = Fruit_s, fill = Fruit_s,
shape = Treatment)) +
geom_point(size =2.5, fill = "white") +
labs(shape = "Test fruit", color = "Selection fruit") +
scale_shape_manual(values = c(16, 15, 17)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96")) +
theme_LO_sober
legend_trade <- lemon::g_legend(legend_tradevide)
Slopeestimates_logchange_pairwise_errorbar <- cowplot::ggdraw() +
cowplot::draw_plot(CheCran_G7 + theme(legend.position = "none"),
x = 0.01, y = 0.5, width = 0.22, height = 0.45) +
cowplot::draw_plot(CranStraw_G7 + theme(legend.position = "none"),
x = 0.31, y = 0.5, width = 0.22, height = 0.45) +
cowplot::draw_plot(StrawChe_G7 + theme(legend.position = "none"),
x = 0.61, y = 0.5, width = 0.22, height = 0.45) +
cowplot::draw_plot(legend_trade, x = 0.85, y = 0.5, width = 0.1, height = 0.1) +
cowplot::draw_plot(CheCran_G29 + theme(legend.position = "none"),
x = 0.01, y = 0, width = 0.22, height = 0.45) +
cowplot::draw_plot(CranStraw_G29 + theme(legend.position = "none"),
x = 0.31, y = 0, width = 0.22, height = 0.45) +
cowplot::draw_plot(StrawChe_G29 + theme(legend.position = "none"),
x = 0.61, y = 0, width = 0.22, height = 0.45) +
cowplot::draw_plot_label(c("Intermediate phenotyping step", "A", "B", "C", " ",
"Final phenotyping step", "D", "E", "F", " "),
x = c(0.30, 0.01, 0.30, 0.61, 0.92, 0.250, 0.01, 0.30, 0.61, 0.92),
y = c(0.99, 0.95, 0.95, 0.95, 0.95, 0.49, 0.45, 0.45, 0.45, 0.45),
hjust = c(-0.25, -0.25, -0.25, -0.25, -0.25, -0.75, -0.75, -0.75, -0.75, -0.75),
vjust = c(1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5),
size = 16)
Slopeestimates_logchange_pairwise_errorbar
#
# #
#
cowplot::save_plot(file =here::here("figures", "FIG_Correlation.pdf"),
Slopeestimates_logchange_pairwise_errorbar,
base_height = 18/cm(1), base_width = 34/cm(1), dpi = 610)5 SA AND LOCAL ADAPTATION
5.1 Analysis fitness change
#####
##### G7
#####
lm_val_G7 = lm(logchange ~ Treatment + Line + SA + Treatment:Fruit_s,
weights = N, data = data_logchange[data_logchange$Generation=="7",])
Fratio = anova(lm_val_G7)[3, 3]/anova(lm_val_G7)[4, 3]
pvalue = 1 - pf(Fratio, anova(lm_val_G7)[3, 1], anova(lm_val_G7)[4, 1])
pvalue## [1] 0.9671626Fratio## [1] 0.001997209anova(lm_val_G7)[3, 1]## [1] 1anova(lm_val_G7)[4, 1]## [1] 3#####
##### G29
#####
lm_val_G29 = lm(logchange ~ Treatment + Line + SA + Treatment:Fruit_s,
weights = N, data = data_logchange[data_logchange$Generation=="29",])
Fratio = anova(lm_val_G29)[3, 3]/anova(lm_val_G29)[4, 3]
pvalue = 1 - pf(Fratio, anova(lm_val_G29)[3, 1], anova(lm_val_G29)[4, 1])
pvalue## [1] 0.1877934Fratio## [1] 2.888168anova(lm_val_G29)[3, 1]## [1] 1anova(lm_val_G29)[4, 1]## [1] 3